q124/random_k4/random_4.md

Encyclopedic VQA:
Visualquestionsaboutdetailedpropertiesoffine-grainedcategories
ThomasMensink†,∗ JasperUijlings†,∗ LluisCastrejon∗ ArushiGoel‡
mensink@google.com jrru@google.com lluisc@google.com goel.arushi@gmail.com
FelipeCadar‡ HowardZhou∗ FeiSha∗ Andre´ Araujo∗
cadar@dcc.ufmg.br howardzhou@google.com fsha@google.com andrearaujo@google.com
VittorioFerrari∗
vittoferrari@google.com
Abstract
We propose Encyclopedic-VQA, a large scale visual
question answering (VQA) dataset featuring visual ques-
tions about detailed properties of fine-grained categories
and instances. It contains 221k unique question+answer
pairs each matched with (up to) 5 images, resulting
in a total of 1M VQA samples. Moreover, our dataset
comes with a controlled knowledge base derived from
Wikipedia, marking the evidence to support each answer. Figure1:Oneofthetwoanswersaboveiswrong,doyouknow
Empirically, we show that our dataset poses a hard whichone? Encyclopedicquestionsaboutdetailedpropertiesof
challenge for large vision+language models as they per- fine-grainedentitiesaredifficult.Notonlyforhumans,butalsofor
form poorly on our dataset: PaLI [14] is state-of-the-art largeVLMs.PaLI[14]failstoanswerbothquestionscorrectly.
on OK-VQA [37], yet it only achieves 13.0% accuracy
on our dataset. Moreover, we experimentally show
Answeringthesequestionscorrectlyrequiresknowledgeof
that progress on answering our encyclopedic questions
detailed properties (i.e. symbol of which city, year of au-
can be achieved by augmenting large models with a
tomation) of fine-grained categories (‘Pinus Pinea’) or in-
mechanism that retrieves relevant information from the
stances (‘Point Reyes Lighthouse’). We hypothesize that
knowledge base. An oracle experiment with perfect re-
this type of encyclopedic knowledge is hard for VLMs to
trievalachieves87.0%accuracyonthesingle-hopportion
properlyencodeinitsmodelparametersbecausesuchlong-
of our dataset, and an automatic retrieval-augmented
tail information occurs rarely in its training data (i.e. the
prototypeyields48.8%.Webelievethatourdatasetenables
web). Additionally,VLMsproducesuchincorrectanswers
future research on retrieval-augmented vision+language
generally with high confidence, while these answers are
models. It is available at https://github.com/
hardforuserstoverifysincethemodeldoesnotprovideany
google-research/google-research/tree/
explanations. Theanswerto Fig.1(left) iscorrect; (right)
master/encyclopedic_vqa.
shouldbe1975. PaLI[14]predictsbothincorrectly.
Bothproblemscanbeaddressedbyretrieval-augmented
1.Introduction models, which base their predictions on knowledge re-
trieved from a database. These models recently gained
Recently, large Vision+Language models (VLMs) have popularity in NLP (e.g. [9, 23, 30, 33]), and some early
demonstrated impressive performance on Visual Question multi-modal models also exist [22, 26, 36, 52]. Retrieval-
Answering (VQA) benchmarks [7, 14, 24, 57]. However, augmentedmodelsarewell-suitedforencyclopedicknowl-
Fig.1showstwotypicalexampleswheresuchmodelsfail. edge since retrieving the correct entry from a knowledge
†Equalcontribution.‡WorkdoneduringinternshipatGoogle. base greatly facilitates constructing the right answer. Fur-
∗GoogleResearch thermore, the retrieved piece of knowledge provides attri-
3202
luJ
42
]VC.sc[
2v42290.6032:viXra

Question Type
skramdnaL
Templated Automatic Automatic - multi-answer 2-Hop
Q: Who founded this monastery? Q: When was the first permanent Q: What fish can be found in this lake? Q: What amusement park is located in
settlement made at this valley? the city where this square is located?
A: Prince Constantin Brâncoveanu A: 1864 A: trout, lake char A: Tivoli Gardens
C: Horezu monastery C: Clover valley C: Úlfljótsvatn C: Rådhuspladsen, Copenhagen
dlroW
larutaN
Q: How old does this reptile become? Q: How many feet tall does this tree Q: Where is this bird found? Q: How many national park service maintained
grow to? sites are in the state where this plant grows?
A: 40 years A: 7 to 13 A: Colombia, Venezuela, Ecuador A: 24
C: Gila monster C: Acacia paradoxa C: Boissonneaua C: Chorizanthe rigida
Figure2: ExampleVQAannotationsfordifferentquestiontypes. EachexampleconsistsofanimageI,aquestionQandtheanswer
A.WealsoshowthecategoryCofthesubjectofthequestion.Asattribution,weprovideasectionwithintheWikipediapageofCwhich
supportstheanswer.OurEncyclopedic-VQAdatasethasatotalof1M(I,Q,A)triplets.
OK-VQA A-OKVQA FVQA KVQA S3VQA
Q: What sort of vehicle uses Q: What does the man who Q: What furniture in this image Q: Who is to the left of Hillary Q: What is the process by
this item? sits have trouble doing? can I lie on? Clinton? which this insect develops?
A: firetruck A: walking A: sofa A: Aamir Khan A: complete metamorphosis
Q: When was this piece of Q: What could block the Q: Which transportation way in Q: Who among the people in the Q: Where was this weapon used
sporting equipment invented? washer’s door? this image is cheaper than taxi? image is the eldest? to attack the French camp?
A: 1926 A: stove A: bus A: Person in the left A: Crecy
Figure3: TypicalexamplesofVQAdatasetswhichrequireknowledgebeyondtheimage.
bution to the answer by design. This increases model in- quiringknowledgeofdetailedproperties,butmostlyabout
terpretabilityandthereforehumantrust,hasapplicationsto coarserbasic-levelcategories[45](e.g.Fig.3bottom-left).
fairnessandhelpsdiagnosinganddebuggingmodelerrors. Thereareafewotherdatasetswhichtargetdetailedproper-
To drive progress on handling encyclopedic knowledge ties[27,47]. Butboth[27,47]lackattributionannotations,
and attribution in VQA we need a suitable dataset. The [27]isverysmall(Tab.1right-mostcolumn),and[47]fo-
popular OK-VQA [37] and A-OKVQA [46] datasets fo- cusesonlyoncelebrities(Fig.3fourthcolumn). Henceno
cus on questions requiring knowledge outside the query currentVQAdatasetisfullysatisfactory.
image. However, the majority of questions require com- In this paper we introduce the Encyclopedic-VQA
monsense knowledge (e.g. Fig. 3 top-row, two left-most dataset, which offers several attractive features (Fig. 2,
examples), a type of knowledge for which current VLMs Tab.1). Ourdatasetasksquestionsaboutfine-grainedcat-
are powerful in both theory and practice [7, 14, 24, 57]. egories from iNaturalist 2021 [51] (Fig. 2 bottom row)
These datasets [37, 46] also include some questions re- andinstancesfromtheGoogleLandmarksDatasetv2[56]

OK-VQA[37] A-OKVQA[46] FVQA[53] S3VQA[27] KVQA[47] Encyclopedic-VQA(ours)
Trulymultimodal
|                                           |     |     |     | ++  |     | ++  | ++  | ++  |     | ++  |     | ++  |     |
| ----------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Encyclopedic                              |     |     |     | ±   |     | -   | -   | +   |     | ++  |     | ++  |     |
| (cid:44)→Detailedproperties               |     |     |     | +   |     | ±   | -   | ++  |     | ++  |     | ++  |     |
| (cid:44)→fine-grainedcategories/instances |     |     |     | -   |     | -   | -   | +   |     | ++  |     | ++  |     |
| Controlledknowledgebase                   |     |     |     | --  |     | --  | +   | ±   |     | ±   |     | ++  |     |
| (cid:44)→answersupportedbyKB              |     |     |     | --  |     | --  | ++  | ++  |     | ++  |     | ++  |     |
(cid:44)→KBprovided
|                      |     |     |     | --  |     | --  | ++  | --  |     | ++  |     | ++  |     |
| -------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| (cid:44)→KBfree-form |     |     |     | --  |     | --  | --  | --  |     | --  |     | ++  |     |
| (cid:44)→attribution |     |     |     | --  |     | ±   | ++  | --  |     | --  |     | ++  |     |
| Scale                |     |     |     | ±   |     | +   | --  | --  |     | ++  |     | ++  |     |
| Two-hop              |     |     |     | --  |     | --  | --  | --  |     | ++  |     | +   |     |
Subject various various various various celebrities fine-grainedspecies,landmarks
| NumberoftextquestionsQ           |     |     |     | 14k |     | 25k | 6k  | 7k  |     | 183k |     | 221k   |     |
| -------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---- | --- | ------ | --- |
| NumberofimagesI                  |     |     |     | 14k |     | 24k | 2k  | 7k  |     | 25k  |     | 514k   |     |
| NumberofuniqueVQAtriplets(I,Q,A) |     |     |     | 14k |     | 25k | 6k  | 7k  |     | 183k |     | 1,036k |     |
Table1: ComparisonofrecentVQAdatasets.WecomparerecentVQAdatasetswithourproposeddatasetonVQAdesignprinciples.
(Fig. 2 top row). We construct questions about detailed that specific part of the knowledge base which supports it.
properties based on Wikipedia (e.g. founder of building, Hencetheknowledgebaseisanintegralpartofourdataset,
maximumageofanimal). Asaconsequence,allquestions whichenablesmeasuringwhetheramodelanswersaques-
in our dataset are about encyclopedic knowledge. Further- tion correctly for the right reason. For generality we want
more, we provide a controlled knowledge base suited to thisknowledgebasetobefree-formtextandtocontainim-
answerthesequestions: 2MWikipediapagesconsistingof ages. (4) Scale. The dataset should be large. Dataset size
free-formtextandimages[48]. Wealsomarkground-truth hasalwaysmatteredandthisisevenmoretruewiththein-
attributionforeachansweratthegranularitylevelofasec- creasinglylargeVLMmodels(e.g.[6,14,28,60,39]). (5)
tion within a Wikipedia page. Importantly, our dataset is Two-hop. Aportionofourquestionsshouldrequireknowl-
collected at scale: we have 221k unique question+answer edge from multiple different documents from the knowl-
pairseachmatchedwitharound5images,resultinginato- edgebase. Includingsuchcomplextwo-hopquestions[58]
talof1Mexamples.Thismakesourdatasetthelargestofits leavessubstantialheadroomforfuturemodeldevelopment.
| kind. Finally, | manyof | ourquestions |     | arecomplex |     | two-hop |     |     |     |     |     |     |     |
| -------------- | ------ | ------------ | --- | ---------- | --- | ------- | --- | --- | --- | --- | --- | --- | --- |
3.RelatedWork
questions[58],whichrequiremultipledifferentdocuments
fromtheknowledgebasetosolve(Fig.2right).
VisualQuestionAnswering(VQA).DAQUAR[35],FM-
| We validate | the usefulness |     | of our | dataset | through | sev- |     |     |     |     |     |     |     |
| ----------- | -------------- | --- | ------ | ------- | ------- | ---- | --- | --- | --- | --- | --- | --- | --- |
IQA[20],VisualMadlibs[59],VQAv1[8]andVQAv2[21]
| eralexperiments. | Inparticular,wedemonstratethatalarge |        |                  |             |        |            |           |          |             |                |     |            |        |
| ---------------- | ------------------------------------ | ------ | ---------------- | ----------- | ------ | ---------- | --------- | -------- | ----------- | -------------- | --- | ---------- | ------ |
|                  |                                      |        |                  |             |        |            | are early | VQA      | datasets.   | These datasets |     | mostly ask | visual |
| VLM (PaLI        | [14]) which                          | yields | state-of-the-art |             |        | results on |           |          |             |                |     |            |        |
|                  |                                      |        |                  |             |        |            | questions | that can | be answered | based          | on  | the query  | image  |
| OK-VQA           | [37] performs                        | poorly | on               | our dataset | (13.0% | ac-        |           |          |             |                |     |            |        |
andgenericknowledgesuchasbasic-levelcategoryrecog-
| curacy). | Next we demonstrate |     | through |     | an oracle | experi- |     |     |     |     |     |     |     |
| -------- | ------------------- | --- | ------- | --- | --------- | ------- | --- | --- | --- | --- | --- | --- | --- |
nition(e.g.‘cat’),countingitems,colors,etc.
mentthatretrieval-augmentedmodelscanyield87.0%ac-
|         |                 |     |        |       |        |           | Knowledge-based |       | VQA.         | Tab. | 1 and   | Fig. 3 compare |     |
| ------- | --------------- | --- | ------ | ----- | ------ | --------- | --------------- | ----- | ------------ | ---- | ------- | -------------- | --- |
| curacy. | Finally, we use | an  | online | image | search | engine to |                 |       |              |      |         |                |     |
|         |                 |     |        |       |        |           | datasets        | which | are designed | to   | require | knowledge      | not |
buildanautomaticretrieval-augmentedprototype,reaching
|                  |                         |          |       |          |             |           | presentintheimage[27,37,46,47,53]. |            |                                   |      |          | Inparticular,OK- |     |
| ---------------- | ----------------------- | -------- | ----- | -------- | ----------- | --------- | ---------------------------------- | ---------- | --------------------------------- | ---- | -------- | ---------------- | --- |
| 48.8% accuracy.  | We                      | conclude | that  | (1) our  | dataset     | poses a   |                                    |            |                                   |      |          |                  |     |
|                  |                         |          |       |          |             |           | VQA [37]                           | and        | A-OKVQA                           | [46] | mostly   | require common-  |     |
| strong challenge | in VQA                  | and      | is in | fact too | hard        | for stan- |                                    |            |                                   |      |          |                  |     |
|                  |                         |          |       |          |             |           | senseknowledge.                    |            | Inaddition,somequestions(18%)inA- |      |          |                  |     |
| dard VLMs;       | (2) retrieval-augmented |          |       | VLMs     | demonstrate | a         |                                    |            |                                   |      |          |                  |     |
|                  |                         |          |       |          |             |           | OKVQA                              | do require | knowledge                         | of   | detailed | properties,      | but |
strongpotentialforaddressingencyclopedicknowledge;(3)
|     |     |     |     |     |     |     | about basic-level |     | categories. | Finally, | 3%  | of the questions |     |
| --- | --- | --- | --- | --- | --- | --- | ----------------- | --- | ----------- | -------- | --- | ---------------- | --- |
ourresultsleavesignificantheadroomforfurtherresearchto
|     |     |     |     |     |     |     | requireknowledgeaboutphysics. |     |     |     | Inthispaperwecreatea |     |     |
| --- | --- | --- | --- | --- | --- | --- | ----------------------------- | --- | --- | --- | -------------------- | --- | --- |
improveretrieval-augmentedVLMs.
datasetwithquestionsexclusivelyaboutdetailedproperties
|     |     |     |     |     |     |     | of fine-grained |     | categories | and instances |     | (Fig. 2). | We be- |
| --- | --- | --- | --- | --- | --- | --- | --------------- | --- | ---------- | ------------- | --- | --------- | ------ |
2.Designprinciplesforourdataset
|     |     |     |     |     |     |     | lieve that | such encyclopedic |     | questions | truly | require | access |
| --- | --- | --- | --- | --- | --- | --- | ---------- | ----------------- | --- | --------- | ----- | ------- | ------ |
We create our VQA dataset with the following desired toaknowledgebasetobeanswered. Infact,wereleasethe
properties in mind: (1) Truly Multimodal. The questions knowledgebasealongwithourdataset,whereasnoexplicit
should not be answerable without the image or the textual knowledgebasewasinvolvedinthecreationof[37,46].
question [21]. (2) Encyclopedic. The questions should SomeexistingVQAdatasetsaresupportedbyaknowl-
be about detailed properties of fine-grained categories or edgebase[27,47,53]. FVQA[53]isaboutcommonsense
instances; a type of questions which are problematic for knowledge. KVQA [47] asks detailed properties about
vanilla VLMs [7, 14, 24, 57]. (3) Controlled knowledge celebrities. Both[47,53]arebasedonastructuredknowl-
base. Each answer in our dataset should be attributable to edge base (RDF triplets). Instead, our knowledge base is

| readilyavailablefree-formWikipediatext,whichhasmore |                                        |     |     |     |     |     |     | 4.Dataset |     |     |     |     |     |     |
| --------------------------------------------------- | -------------------------------------- | --- | --- | --- | --- | --- | --- | --------- | --- | --- | --- | --- | --- | --- |
| information.                                        | Furthermore,KVQAisexclusivelyaboutpeo- |     |     |     |     |     |     |           |     |     |     |     |     |     |
Wenowdetailtheconstructionofourdatasetwhilefol-
| ple, which | requires         | specialized |              | methods | based | on      | face de- |             |              |            |          |           |              |         |
| ---------- | ---------------- | ----------- | ------------ | ------- | ----- | ------- | -------- | ----------- | ------------ | ---------- | -------- | --------- | ------------ | ------- |
|            |                  |             |              |         |       |         |          | lowing the  | design       | principles |          | (Sec. 2). | To achieve   | scale   |
| tection    | and recognition. |             | In contrast, |         | our   | dataset | offers a |             |              |            |          |           |              |         |
|            |                  |             |              |         |       |         |          | we automate | construction |            | whenever |           | possible. We | use hu- |
broaderrangeoftopics,includinganimals,plantsandland-
manannotatorstoensurequalityandtoprovideinformation
| marks. | S3VQA | [27] | is the most | related | work | to  | ours. It |                                  |     |     |     |     |                 |     |
| ------ | ----- | ---- | ----------- | ------- | ---- | --- | -------- | -------------------------------- | --- | --- | --- | --- | --------------- | --- |
|        |       |      |             |         |      |     |          | whichwecouldnotgetautomatically. |     |     |     |     | Wesimplifyhuman |     |
buildsoncategoriesofOpenImages[32],somefine-grained
annotationtasksasmuchaspossible,whichincreasesboth
| and others | more | basic-level. |     | They automatically |     | generate |     |     |     |     |     |     |     |     |
| ---------- | ---- | ------------ | --- | ------------------ | --- | -------- | --- | --- | --- | --- | --- | --- | --- | --- |
thequalityandefficiencyoftheirwork.
| questions | from | Wikipedia | articles, |     | which | by construction |     |     |     |     |     |     |     |     |
| --------- | ---- | --------- | --------- | --- | ----- | --------------- | --- | --- | --- | --- | --- | --- | --- | --- |
WedefineaunitforourVQAtaskasatriplet(I,Q,A).
| are about                               | detailed  | properties. |          | Then | they let   | experts      | manu- |                                        |     |     |     |                          |           |     |
| --------------------------------------- | --------- | ----------- | -------- | ---- | ---------- | ------------ | ----- | -------------------------------------- | --- | --- | --- | ------------------------ | --------- | --- |
|                                         |           |             |          |      |            |              |       | Themulti-modalquestionfeaturesanimageI |     |     |     |                          | andaccom- |     |
| allyselectandrephraserelevantquestions. |           |             |          |      |            | Ourpaperalso |       |                                        |     |     |     |                          |           |     |
|                                         |           |             |          |      |            |              |       | panyingtextualquestionQ.               |     |     |     | ThesubjectofthequestionQ |           |     |
| automatically                           | generates |             | QA pairs | from | Wikipedia. |              | How-  |                                        |     |     |     |                          |           |     |
appearsinI
|                                   |     |     |     |     |                 |     |     |     | andisacoreconceptofourwork. |     |     |     | Wereferto |     |
| --------------------------------- | --- | --- | --- | --- | --------------- | --- | --- | --- | --------------------------- | --- | --- | --- | --------- | --- |
| ever,wegobeyond[27]inseveralways: |     |     |     |     | (1)ourdatasetis |     |     |     |                             |     |     |     |           |     |
thecategoryofthesubjectasC(e.g.‘HorezuMonastery’or
muchlarger(7kvs1MVQAtriplets),(2)weincludemore
|           |           |            |        |        |         |      |          | ‘GilaMonster’inFig.                                 |     |       | 2-left). | TheanswerAispurelytex- |            |           |
| --------- | --------- | ---------- | ------ | ------ | ------- | ---- | -------- | --------------------------------------------------- | --- | ----- | -------- | ---------------------- | ---------- | --------- |
| complex   | multi-hop | questions, |        | (3) we | release | the  | knowl-   |                                                     |     |       |          |                        |            |           |
|           |           |            |        |        |         |      |          | tual. Ourdatasetalsorecordstheevidenceforeachanswer |     |       |          |                        |            |           |
| edge base | along     | with       | the QA | pairs. | This    | also | includes |                                                     |     |       |          |                        |            |           |
|           |           |            |        |        |         |      |          | in the knowledge                                    |     | base, | as a     | section                | within the | Wikipedia |
ground-truthanswerattributionintheformofthesupport-
|     |     |     |     |     |     |     |     | pageforC | wheretheanswerisfound. |     |     |     | Incontrasttomany |     |
| --- | --- | --- | --- | --- | --- | --- | --- | -------- | ---------------------- | --- | --- | --- | ---------------- | --- |
ingWikipediasection.
|     |          |         |      |               |     |              |     | existing | VQA | datasets, | we  | record only | a single | answer, |
| --- | -------- | ------- | ---- | ------------- | --- | ------------ | --- | -------- | --- | --------- | --- | ----------- | -------- | ------- |
| The | InfoSeek | dataset | [15] | is concurrent |     | to our work. | It  |          |     |           |     |             |          |         |
sinceitisunambiguousgiventheknowledgebase.
| also targets | detailed    | properties   |     | of fine-grained |         | categories, |     |         |     |         |       |              |     |            |
| ------------ | ----------- | ------------ | --- | --------------- | ------- | ----------- | --- | ------- | --- | ------- | ----- | ------------ | --- | ---------- |
|              |             |              |     |                 |         |             |     | To make | our | dataset | truly | multi-modal, | we  | always re- |
| but does     | not include | multi-answer |     | and             | two-hop | questions,  |     |         |     |         |       |              |     |            |
fertothesubjectofthequestionbyitssupercategory(e.g.
andwascollectedusingadifferentannotationprotocol.
|                              |     |     |     |                    |     |     |     | ‘this monastery’, |      | ‘this | reptile’, | as opposed | to ‘the      | Horezu |
| ---------------------------- | --- | --- | --- | ------------------ | --- | --- | --- | ----------------- | ---- | ----- | --------- | ---------- | ------------ | ------ |
| Automaticquestiongeneration. |     |     |     | AfewVQAdatasetsare |     |     |     |                   |      |       |           |            |              |        |
|                              |     |     |     |                    |     |     |     | Monastery’,       | ‘the | Gila  | monster’, | etc.).     | This ensures | visual |
generated from image captions [44, 13]. While this en- recognitionisrequiredtosolveourtask,astherewouldbe
ablesmuchlargerscale,itresultsinsimplevisualquestions.
manypotentialanswerstothetextualquestionalone(e.g.a
| We build | a portion | of  | our dataset | using | a   | similar | pipeline |     |     |     |     |     |     |     |
| -------- | --------- | --- | ----------- | ----- | --- | ------- | -------- | --- | --- | --- | --- | --- | --- | --- |
differentanswerforeachmonasteryintheworld).
| as [13], | but apply | it to | Wikipedia | pages | instead | to  | obtain |     |     |     |     |     |     |     |
| -------- | --------- | ----- | --------- | ----- | ------- | --- | ------ | --- | --- | --- | --- | --- | --- | --- |
4.1.SupportingDatasets
| high-quality | questions |     | on detailed | properties. |     | As  | another |     |     |     |     |     |     |     |
| ------------ | --------- | --- | ----------- | ----------- | --- | --- | ------- | --- | --- | --- | --- | --- | --- | --- |
difference,weverifythemwithhumanannotators. We build on two of the largest existing datasets with
| Multi-Hop | Reasoning |     | in NLP. | The | NLP | community | has |                         |     |     |                                |     |     |     |
| --------- | --------- | --- | ------- | --- | --- | --------- | --- | ----------------------- | --- | --- | ------------------------------ | --- | --- | --- |
|           |           |     |         |     |     |           |     | fine-grainedcategories: |     |     | iNaturalist2021(iNat21)[51]and |     |     |     |
many text-only QA datasets (e.g. [19, 29, 31, 38, 42, 50, Google Landmarks Dataset V2 (GLDv2) [56]. iNat21 is
55, 58]). Notably, HotpotQA [58] introduces the concept afine-grainedvisualclassificationdatasetcontaining2.7M
| of‘bridgeentity’whichlinkstworelatedentities. |     |     |     |     |     | Theyuse |     |     |     |     |     |     |     |     |
| --------------------------------------------- | --- | --- | --- | --- | --- | ------- | --- | --- | --- | --- | --- | --- | --- | --- |
imagesdepicting10,000species,groupedinto11supercat-
thistoshowanannotatortworelatedWikipediaparagraphs egories: plants, insects, birds, mammals, etc. GLDv2 is
| from different | pages | and | ask | to create | questions | which | re- |            |             |     |         |      |           |           |
| -------------- | ----- | --- | --- | --------- | --------- | ----- | --- | ---------- | ----------- | --- | ------- | ---- | --------- | --------- |
|                |       |     |     |           |           |       |     | a landmark | recognition |     | dataset | with | 4M images | depicting |
quires knowledge from both paragraphs. We use ‘bridge 200k landmarks. Each landmark is sourced from Wiki-
entities’toautomaticallychaintwoquestionstogetherinto media Commons [5], enabling us to mine the existing
acompoundtwo-hopquestion.
category-hierarchy(allowingtodefinesupercategories,like
Retrieval-augmented models. Our dataset seems partic- bridges, castles, lighthouses, lakes etc.), and to link them
ularly suited for retrieval-augmented models. While pio- to Wikipedia articles. We use these provided annotations
neered in NLP [9, 23, 30, 33], several recent works use to speed up the annotation of our dataset, to identify rele-
retrieval for VQA. KRISP [36] leverages triplets encoding vantknowledgecategories,andtoassignimagesItotextual
facts, categorical knowledge and object relationships in a questionsQautomatically.
graph-based reasoning framework. KAT [22] uses Wiki- We create our controlled knowledge base starting from
data triplets, and a reasoning module cross-attends them the WIT dataset [48], which contains 37M image-snippet
withGPT-3answers,theresultbeingfedintoadecoderfor pairs with 11M unique images from Wikipedia. We select
answer generation. InFactuality [52] leverages index im- allimageslinkedtoanEnglishsnippetandthenextendWIT
agestolinkentitiespresentinthequeryimageandWikidata to include the full corresponding Wikipedia article (snap-
triplets to gather facts, which are fed into a UNITER [16] shot of 13 August 2022). This spans 2M English articles.
reasoning module. REVEAL [26]’s external knowledge is We then identify which categories of iNat21 and GLDv2
composedofimage-textpairs,questionansweringpairsand map one-to-one to a single Wikipedia article. We found
knowledgegraphtriplets,whichareusedtoassistagenera- such unique mappings for 80% of the iNat21 and 50% of
tormoduleinproducinganswers. the GLDv2 categories. We only create questions for those

Monastery Horezu Monastery Templated question based upon super category
Horezu Monastery
Question:    Who founded this monastery?
The Horezu Monastery or Hurezi Monastery was founded in 1690 by Prince
|     |     |     | Constantin Brâncoveanu in the town of Horezu, Wallachia, Romania. It is  |     |     |     |     | Answer: |     |     |     |     |
| --- | --- | --- | ------------------------------------------------------------------------ | --- | --- | --- | --- | ------- | --- | --- | --- | --- |
considered to be a masterpiece of "Brâncovenesc style", known for its
|     |     |     | architectural purity and balance, the richness of its sculpted detail, its  |     |     |     |     | Evidence: |     | Copy selected evidence |     |     |
| --- | --- | --- | --------------------------------------------------------------------------- | --- | --- | --- | --- | --------- | --- | ---------------------- | --- | --- |
|     |     |     | treatment of religious compositions, its votive portraits, and its painted  |     |     |     |     |           |     | Not enough evidence    |     |     |
decorative works.
Plant Acacia paradoxa Acacia paradoxa Automated question from wikipedia section
Are the following question/answer pairs relevant and correct?
Description
The large shrub or tree up to 2 to 4 metres (7 to 13 ft) tall and has a similar  Question:  How many feet tall does the acacia  Yes
paradoxa grow to?
|     |     |     | width, it has ribbed branchlets that are often arched downward. It is dense  |     |     |     |     | Answer:  | 7 to 13 |     |     | No  |
| --- | --- | --- | ---------------------------------------------------------------------------- | --- | --- | --- | --- | -------- | ------- | --- | --- | --- |
with foliage; the leaves are actually enlarged petioles known as phyllodes.
They are crinkly and the new ones are covered in hairs. The erect phyllodes
|     |     |     |                                                                           |     |     |     |     | Question:  | What are the new leaves of this plant  |     |     | Yes |
| --- | --- | --- | ------------------------------------------------------------------------- | --- | --- | --- | --- | ---------- | -------------------------------------- | --- | --- | --- |
|     |     |     | are asymettric and have a lanceolate shape and are around 30 millimetres  |     |     |     |     |            | covered in?                            |     |     |     |
(1.18 in) in length and 7 mm (0.276 in) wide. Answer:  hairs No
Figure4: Datacollectionfortemplatedandautomaticallygeneratedsingle-hopquestions. Top: Expertscreatetemplatedquestions
Q based on a super category (e.g. Monastery). Annotators are given the full Wikipedia page for a particular C, and asked to provide
theanswersAandtheevidence. Bottom: QuestionsareautomaticallygeneratedfromaWikipediasectionofC andvalidatedbyhuman
annotators.Inbothprocesses,theannotatorneverseestheimageIcorrespondingtoC.
categorieswithauniquelyidentifiableWikipediaarticle. resolvethespecificcategoryC andamodelsimplyremem-
The combination of iNat21 & GLDv2 with Wikipedia beringQ+Apairscannotsolvethechallenge.
as knowledge base enables creating (I,Q,A) triplets cov- Automaticallygeneratedsingle-hop. Weincreasethedi-
ering the long-tail: questions about detailed properties (on versity of questions in the dataset with automatically gen-
| Wikipedia | text) | of fine-grained | categories | (species | from |                  |     |       |             |          |     |          |
| --------- | ----- | --------------- | ---------- | -------- | ---- | ---------------- | --- | ----- | ----------- | -------- | --- | -------- |
|           |       |                 |            |          |      | erated questions |     | (Fig. | 4). Similar | to [27], | we  | feed the |
iNat21) and instances (landmarks from GLDv2). Hence, Wikipedia article of a category to a question generation
thissatisfiestheencyclopedicdesignprinciple. model [13]. It processes a section of an article at a time,
|                         |     |     |     |     |     | producing                         | a large | number | of Q+A | pairs                  | (typically | 100s), |
| ----------------------- | --- | --- | --- | --- | --- | --------------------------------- | ------- | ------ | ------ | ---------------------- | ---------- | ------ |
| 4.2.Single-hopquestions |     |     |     |     |     | byinvertingstatementsfromthetext. |         |        |        |                        |            |        |
|                         |     |     |     |     |     | Weincludetwofilteringsteps.       |         |        |        | First,werequirethecat- |            |        |
Theanswertoasingle-hopquestioncanbefoundinthe
|                                |     |     |                     |     |     | egory name | of  | C to be | used in the | question. | This | reduces |
| ------------------------------ | --- | --- | ------------------- | --- | --- | ---------- | --- | ------- | ----------- | --------- | ---- | ------- |
| WikipediaarticleofthecategoryC |     |     | whichisthesubjectof |     |     |            |     |         |             |           |      |         |
thequestion.Weconstructsuchquestionsintwoalternative thenumberofquestionsby20×,andensuresthattheques-
|     |     |     |     |     |     | tion is about | C   | (and not | about another | entity | found | in the |
| --- | --- | --- | --- | --- | --- | ------------- | --- | -------- | ------------- | ------ | ----- | ------ |
ways: templatedandautomaticallygenerated.
|                      |     |                              |     |     |     | snippet        | of text). | Second,   | to increase | diversity  | we           | remove |
| -------------------- | --- | ---------------------------- | --- | --- | --- | -------------- | --------- | --------- | ----------- | ---------- | ------------ | ------ |
| Templatedsingle-hop. |     | Weuseofthesupercategoryanno- |     |     |     |                |           |           |             |            |              |        |
|                      |     |                              |     |     |     | near identical |           | questions | and limit   | the number | of questions |        |
tationsavailableinthesupportingdatasets(e.g.reptilesfor
perWikipediasection.
| iNat21, monasteries |     | for GLDv2). | For each | super | category |     |     |     |     |     |     |     |
| ------------------- | --- | ----------- | -------- | ----- | -------- | --- | --- | --- | --- | --- | --- | --- |
wemanuallydefineseveralquestions,likeWhofoundedthis The human annotation task is now extremely simple:
validatewhethertheQ+Apairisrelevantandcorrect,given
| monastery?. | Wethenaskhumanannotatorstoanswerthese |     |     |     |     |     |     |     |     |     |     |     |
| ----------- | ------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
questionsforaparticularspecies/landmarkC (e.g.Horezu theWikipediasectionusedtogenerateit. AquestionQis
|     |     |     |     |     |     | relevant | if it is | is well-formed, | makes | sense | semantically, |     |
| --- | --- | --- | --- | --- | --- | -------- | -------- | --------------- | ----- | ----- | ------------- | --- |
Monastery),showingthemtherelevantWikipediapage(see
Fig. 4). Hence, theannotatorsdonothavetorecognizeC and is expected to have an unambiguous answer. The an-
in an image, which would require expert knowledge. In- swer A is correct if it is supported by the given Wikipedia
|     |     |     |     |     |     | section. | Finally, | wecandirectlyusetheWikipediasection |     |     |     |     |
| --- | --- | --- | --- | --- | --- | -------- | -------- | ----------------------------------- | --- | --- | --- | --- |
stead,theyjusthavetoidentifywhethertheanswerisinthe
Wikipediaarticle. Ifyes,theymarktheevidenceforit(for usedtogeneratetheQApairasevidencefortheanswer(at-
tributionforfree!)
| attribution). | If  | no, we discard | that question | (as | it cannot |     |     |     |     |     |     |     |
| ------------- | --- | -------------- | ------------- | --- | --------- | --- | --- | --- | --- | --- | --- | --- |
be answered with our knowledge base and it would break About 80% of the questions sent to annotators are val-
thecontrolledknowledgebaseprinciple). Westartwith10 idated positively. The questions contain (by design) the
questions per category, resulting in 4.3 questions on aver- name of the category C, and we rephrase them using a
ageperspecieswithavalidanswerforiNat21,and3.4per carefully-promptedFLAN[18]versionofPaLM[17]tore-
|     |     |     |     |     |     | place the | mention | of  | C by its super | category, | which | pro- |
| --- | --- | --- | --- | --- | --- | --------- | ------- | --- | -------------- | --------- | ----- | ---- |
landmarkforGLDv2(seeexamplesinFig.2firstcolumn).
E´glise
NowwehaveatextualquestionQandananswerA. To duces Q. For example, the question Where is the
form a complete multi-modal question, we pair Q with an Saint-Cannat located? is rephrased by PaLM to Where is
|     |     |     |     |     |     | thischurchlocated? |     | AnimageI |     | fromC |     |     |
| --- | --- | --- | --- | --- | --- | ------------------ | --- | -------- | --- | ----- | --- | --- |
imageI ofC fromthesupportingdatasets. NotethatanyQ fromasupporting
occursmultipletimesinthedatasetwithdifferentanswers. datasetisthenassociatedtothisquestion.
HencebydesigntheaccompanyingimageI isnecessaryto Multi-answer questions. For many properties of a cat-

Q: What is the main competitor for food for this animal? Q: What is the population size of this animal? Chained two hop question with bridge: spotted hyena
What is the population
size of the main
competitor for food of
this animal?
Q: What is the main competitor for food for this animal? Q: What is the population size of this animal? Chained question with bridge: spotted hyena
|     | Lion |     |     | Spotted hyena |     |     |     |
| --- | ---- | --- | --- | ------------- | --- | --- | --- |
Predatory competition
|     |     |     |     | The spotted hyena (Crocuta crocuta), also  |     | What is the population  |     |
| --- | --- | --- | --- | ------------------------------------------ | --- | ----------------------- | --- |
Lions and spotted hyenas occupy a similar  known as the laughing hyena ... It is listed  size of the main
as being of least concern by the IUCN on
ecological niche and compete for prey and  a c c o u n t  o f  it s  w id e s p r e a d   ra n g e   a n d   la r g e  competitor for food of
c a r ri o n ;   a   re v ie w   o f   d a t a   a c ro ss   s e v e ral  n u m b e r s  e s t im a te d   b e t w e e n  2 7 , 0 0 0   a n d   this animal?
|     | s tu d i e s   i n d ic                | a te s   a   d i e t a r y  o ve rl a p   o f  |     |                                         |     |     |     |
| --- | -------------------------------------- | ---------------------------------------------- | --- | --------------------------------------- | --- | --- | --- |
|     | 58.6%. Lions typically ignore hyenas…  |                                                |     | 47,000 individuals. The species is, ... |     |     |     |
Figure 5: Illustrationoftwo-hopquestiongenerationusingbridgeentities. Thebridgeentity,e.g.spottedhyena,isananswertoa
single-hopquestionwhichhasitsownentryintheknowledgebase. Hencewecanaskasecondsingle-hopquestionaboutit. Thesetwo
questionsarethenchainedintoatwo-hopquestion.
egory C the response should be a list with multiple an- pair is combined with (up to) 5 images I showing differ-
swers. For example What fish can be found in this lake? ent instances of the same category C from the supporting
orWhereisthisbirdfound?,couldbeansweredwithalist datasets. Thisincreasesvisualdiversityandintotalweuse
offish(orcountries,respectively;Fig.2,thirdcolumn).For 514kuniqueimages. Thereareintotal15ktextualsingle-
these kinds of questions we define the multi-answer ques- hoptemplatedquestions,and158kautomaticallygenerated
tion type. We filter the automatically generated QA pairs questions. Moreover,thedatasetcontains25kmulti-answer
for questions likely to have multiple answers, and extract questionsand22ktwo-hopquestions.
| aninitiallist. | Thenweasktheannotatorstocompleteand |     |     |     |     |     |     |
| -------------- | ----------------------------------- | --- | --- | --- | --- | --- | --- |
Lookingindetailatthe221kQ+Apairs,175kofthem
verifythelistwithallthepossibleanswers.
haveauniqueQ(i.e.nostringmatchwithanyotherques-
4.3.Two-hopquestions tion). Moreover, there are 95k unique answers, of which
|     |     |     |     | 73koccuronlyonce. | Theremaining22kanswersfollowa |     |     |
| --- | --- | --- | --- | ----------------- | ----------------------------- | --- | --- |
Two-hopquestionsaredefinedasrequiringtworeason- Zipf’slaw:11kanswersoccurtwice,2kanswersoccur10+
| ing steps | to obtain the | final answer. | Here we focus | on  |     |     |     |
| --------- | ------------- | ------------- | ------------- | --- | --- | --- | --- |
times,andlessthan400answersoccurmorethan50times.
chained two-hopquestions,whichrequiretwoconsecutive
retrievalsteps. Toconstructsuchquestionsatscale,weuse Train / Val / Test splits. To allow for evaluating differ-
thenotionofabridgeentity[58]: iftheanswertoasingle- entpropertiesofthedataset, wecarefullydesignitstrain/-
hop question is an entity with its own Wikipedia page, it val/test splits. First, there is no overlap in the images I
servesasabridgeentity,aboutwhichwecanaskasecond usedinthetrain,val,ortestsplits. ForGLDv2,wesample
single-hopquestion,seeFig.5. images for all our splits from their train-clean split, which
We automatically identify bridge entities from the avoids most noisy labels in their dataset. For iNat21, we
single-hopanswers(Sec.4.2),thenmanuallydiscardwords sampleourtrainimagesfromtheirtrainset,andourvaland
like yes, blue, and heavy which would yield very artificial test images from their validation set (their test set annota-
multi-hop questions. We then automatically generate and tionsarenotpubliclyavailable). Next, abouttwo-thirdsof
validate questions for these bridge entities using our auto- thequestionsQandhalfoftheanswersAinourvalandtest
maticallygeneratedsingle-hoppipeline. splits do not occur in our train split. Finally, roughly 17%
Wecreatethefinaltwo-hopquestionautomaticallyusing ofthesubjectcategoriesC inourvalandtestsplitsarenot
PaLM, by feeding it the two single-hop questions with an used in our train split. This allows for analyzing different
appropriateprompt.Unfortunately,thesetwo-hopquestions desirablepropertiesofVQAmodelsonourdataset.
areoftenincorrect.Therefore,wecreateasecondpromptto
validatethetwo-hopquestion:PaLMisaskedtoanswerthe
two-hopquestionusingthetwoinitialsingle-hopquestions
| with answers | as context. | If the predicted | answer is | iden- |                    |                      |     |
| ------------ | ----------- | ---------------- | --------- | ----- | ------------------ | -------------------- | --- |
|              |             |                  |           |       | Numberof(Q,A)pairs | Total(I,Q,A)triplets |     |
tical to the answer of the second single-hop question, then Train Val Test Train Val Test
the two-hop question is validated and kept in our dataset; Templated 13,928 400 1,000 66,535 1,827 1,000
|                                   |                  |                     |              | Automatic   | 153,441 1,750 | 2,750 737,114 | 8,025 2,750 |
| --------------------------------- | ---------------- | ------------------- | ------------ | ----------- | ------------- | ------------- | ----------- |
| otherwise,                        | it is discarded. | This increases      | the accuracy | of          |               |               |             |
|                                   |                  |                     |              | MultiAnswer | 23,929 400    | 1,000 112,736 | 1,844 1,000 |
| thetwo-hopquestionssubstantially. |                  | Examplesofvalidated |              |             |               |               |             |
|                                   |                  |                     |              | TwoHop      | 21,040 400    | 1,000 99,866  | 1,895 1,000 |
two-hopquestionsaregiveninFig.2(right).
|     |     |     |     | Total | 212,338 2,950 | 5,750 1,016,251 | 13,591 5,750 |
| --- | --- | --- | --- | ----- | ------------- | --------------- | ------------ |
4.4.Datasetstatistics
Table2:
|     |     |     |     | DatasetStatistics. |     | Wereportthenumberof(question, |     |
| --- | --- | --- | --- | ------------------ | --- | ----------------------------- | --- |
Our dataset contains in total 1M (I,Q,A) triplets answer)pairs,and(image,question,answer)tripletsfordifferent
(Tab. 2). These are derived from a total of 221k textual question types. In total our dataset contains 1M VQA triplets,
makingitthelargestofitskind.
| Q+Apairsfrom16.7kdifferentcategoriesC. |     |     | EachQ+A |     |     |     |     |
| -------------------------------------- | --- | --- | ------- | --- | --- | --- | --- |

Dataset varietyofQ disparityofQ Model Retrieval OK-VQAAccuracy
|             |     | #uniquebigrams |     |       | avg.cosinedist. |       |       |               | KAT[22]  |      | ✓                   |     | 53.1 |         |       |
| ----------- | --- | -------------- | --- | ----- | --------------- | ----- | ----- | ------------- | -------- | ---- | ------------------- | --- | ---- | ------- | ----- |
|             |     |                |     |       |                 |       |       | REVEAL[26]    |          |      | ✓                   |     | 59.1 |         |       |
| FVQA[53]    |     |                |     | 7.9k  |                 | 0.620 |       |               |          |      |                     |     |      |         |       |
|             |     |                |     |       |                 |       |       | PromptCap[25] |          |      | -                   |     | 60.4 |         |       |
| KVQA[47]    |     |                |     | 12.8k |                 | 0.504 |       |               |          |      |                     |     |      |         |       |
|             |     |                |     |       |                 |       |       |               | PaLI[14] |      | -                   |     | 64.5 |         |       |
| OK-VQA[37]  |     |                |     | 19.3k |                 | 0.843 |       |               |          |      |                     |     |      |         |       |
| S3VQA[27]   |     |                |     | 19.7k |                 | 0.805 |       |               |          |      |                     |     |      |         |       |
|             |     |                |     |       |                 |       | Table | 4:            | OK-VQA:  | PaLI | is state-of-the-art |     | on   | OK-VQA. | It is |
| A-OKVQA[46] |     |                |     | 33.9k |                 | 0.856 |       |               |          |      |                     |     |      |         |       |
thereforeangoodcandidatetoapplytoourEncyclopedic-VQA.
| Encyclopedic-VQA                 |              |              | 257.9k                    |          |          | 0.833    |               |             |     |            |      |      |     |       |         |
| -------------------------------- | ------------ | ------------ | ------------------------- | -------- | -------- | -------- | ------------- | ----------- | --- | ---------- | ---- | ---- | --- | ----- | ------- |
| Table                            | 3: Diversity | of questions | Q                         | measured | in terms | of vari- | 5.Experiments |             |     |            |      |      |     |       |         |
| etyanddisparity(higherisbetter). |              |              | Disparitynumbersaremostly |          |          |          |               |             |     |            |      |      |     |       |         |
|                                  |              |              |                           |          |          |          |               | We evaluate |     | PaLI [14], | PaLM | [17] | and | GPT-3 | [10] on |
takenfrom[46],butwereproducedtheirA-OKVQAresulttoval-
idate our re-implementation. Our dataset has the largest variety our dataset. We apply all models directly and in retrieval-
andisclosetothebestonesondisparity. augmented settings. For evaluation we use the test split
|     |     |     |     |     |     |     | of     | our dataset |              | on the    | single-hop | templated   |             | and automat- |     |
| --- | --- | --- | --- | --- | --- | --- | ------ | ----------- | ------------ | --------- | ---------- | ----------- | ----------- | ------------ | --- |
|     |     |     |     |     |     |     | ically | generated   |              | questions | (except    |             | for Sec.    | 5.6 where    | we  |
|     |     |     |     |     |     |     | also   | evaluate    | multi-answer |           |            | and two-hop | questions). |              | We  |
4.5.QuestionDiversity
measureaccuracyasthepercentageofquestionswherethe
|     |     |     |     |     |     |     | predicted |     | model | answer | matches | the | ground-truth |     | answer. |
| --- | --- | --- | --- | --- | --- | --- | --------- | --- | ----- | ------ | ------- | --- | ------------ | --- | ------- |
WewanttocomparethediversityofourVQAquestions
to other datasets. However, diversity is a broad concept All ground-truth answers and model predictions are pre-
|     |     |     |     |     |     |     | processed |     | following | standard |     | VQA | practices | [21] | to facil- |
| --- | --- | --- | --- | --- | --- | --- | --------- | --- | --------- | -------- | --- | --- | --------- | ---- | --------- |
whichmanifeststhroughacombinationofthreebasicprop-
|         |          |            |             |       |      |          | itate | checking | their | correctness |     | (remove | articles, | punctua- |     |
| ------- | -------- | ---------- | ----------- | ----- | ---- | -------- | ----- | -------- | ----- | ----------- | --- | ------- | --------- | -------- | --- |
| erties: | variety, | disparity, | and balance | [49]. | Here | we focus |       |          |       |             |     |         |           |          |     |
onvarietyanddisparity. tion, etc.). Furthermore, weusethe’BERTMatching’cri-
|     |     |     |     |     |     |     | terion | BEM | [12] | to determine |     | whether | a predicted |     | answer |
| --- | --- | --- | --- | --- | --- | --- | ------ | --- | ---- | ------------ | --- | ------- | ----------- | --- | ------ |
Variety. This refers to the semantic variety spanned by is correct given the question and the ground-truth answer.
| the VQA | questions. | One | simple | measure | of variety | is the |     |            |     |        |     |                  |     |        |        |
| ------- | ---------- | --- | ------ | ------- | ---------- | ------ | --- | ---------- | --- | ------ | --- | ---------------- | --- | ------ | ------ |
|         |            |     |        |         |            |        | BEM | evaluation |     | allows | for | more flexibility |     | in the | answer |
numberofuniquetextualquestionsQ(175kinourdataset).
|                    |      |                                 |           |      |           |         | formulation |     | than  | classical | exact | matching,       |     | coming | much  |
| ------------------ | ---- | ------------------------------- | --------- | ---- | --------- | ------- | ----------- | --- | ----- | --------- | ----- | --------------- | --- | ------ | ----- |
| However,           | this | ignores the                     | fact that | some | questions | are se- |             |     |       |           |       |                 |     |        |       |
|                    |      |                                 |           |      |           |         | closer      | to  | human | judgement |       | of correctness, |     | as is  | shown |
| manticallysimilar. |      | Anothermeasureisthenumberoftop- |           |      |           |         |             |     |       |           |       |                 |     |        |       |
in[12]andinouruserstudyinAppendixB.Weconsideran
icscoveredbythequestions,forwhichwecanusethenum- answercorrectwhenitsBEMscoreis≥0.5. Ifanexample
| ber of | categories | C (16.7k | in our | dataset). | However, | each |     |          |              |     |          |     |            |     |     |
| ------ | ---------- | -------- | ------ | --------- | -------- | ---- | --- | -------- | ------------ | --- | -------- | --- | ---------- | --- | --- |
|        |            |          |        |           |          |      | has | multiple | ground-truth |     | answers, |     | we compute | the | BEM |
datasettypicallyusesadifferentsetofcategoriesatdiffer-
scoreforeachandchoosethemaximum.
entlevelsofgranularity,makingtheircategorycountshard
to compare directly. So instead, we consider the number 5.1.LargeModelswithoutRetrieval
| of unique | bigrams | across | all questions | Q,  | as a | reasonable |       |     |         |     |             |     |      |           |     |
| --------- | ------- | ------ | ------------- | --- | ---- | ---------- | ----- | --- | ------- | --- | ----------- | --- | ---- | --------- | --- |
|           |         |        |               |     |      |            | PaLI. | To  | measure | the | performance |     | of a | large VLM | on  |
approximationofthesemanticspacespannedbyallQ.
Encyclopedic-VQA
|            |     |               |               |     |               |     |            |     |          | we  | use    | PaLI [14]. | It yields | the | state- |
| ---------- | --- | ------------- | ------------- | --- | ------------- | --- | ---------- | --- | -------- | --- | ------ | ---------- | --------- | --- | ------ |
| Disparity. |     | This measures | how different |     | the questions | are |            |     |          |     |        |            |           |     |        |
|            |     |               |               |     |               |     | of-the-art |     | accuracy | on  | OK-VQA | [37]       | (64.5%),  | and | out-   |
from each other. We follow the measure introduced in A- performs retrieval-augmented models such as KAT [22]
| OKVQA    | [46]: | (1) we         | first project | all        | questions | Q into |         |     |            |     |      |         |     |         |        |
| -------- | ----- | -------------- | ------------- | ---------- | --------- | ------ | ------- | --- | ---------- | --- | ---- | ------- | --- | ------- | ------ |
|          |       |                |               |            |           |        | (53.1%) |     | and REVEAL |     | [26] | (59.1%) | by  | a large | margin |
| a common |       | semantic space | using         | a publicly | available | [3]    |         |     |            |     |      |         |     |         |        |
(Tab.4). Thisdemonstratesthatthetypeofknowledgere-
Sentence-BERT model [43]; then (2) we measure the av- quired to solve OK-VQA can be captured by large VLMs.
eragecosinedistancebetweenallquestionpairs(definedas
|           |            |     |             |      |       |           | PaLI | is therefore |     | a good | candidate |     | to verify | whether | large |
| --------- | ---------- | --- | ----------- | ---- | ----- | --------- | ---- | ------------ | --- | ------ | --------- | --- | --------- | ------- | ----- |
| 1− cosine | similarity | by  | [46]). Note | that | while | [46] pro- |      |              |     |        |           |     |           |         |       |
VLMscapturealsoencyclopedicknowledge.WeuseaPaLI
posedthisasagenericmeasureofdiversity,itactuallyonly
modelwith17Bparameterspre-trainedonahugeamountof
measuresdisparityasdefinedin[49].
dataincludingWikipedia(soithasseentheknowledgebase
| Results. |     |                |     |           |     |            | containingtheanswersforourdataset). |     |     |     |     |     | OurPaLImodelis |     |     |
| -------- | --- | -------------- | --- | --------- | --- | ---------- | ----------------------------------- | --- | --- | --- | --- | --- | -------------- | --- | --- |
|          | We  | report variety | and | disparity | for | knowledge- |                                     |     |     |     |     |     |                |     |     |
basedVQAdatasetsinTab.3. Ourdatasetoffersthelargest additionallyfine-tunedonOK-VQAandthereforeparticu-
questionvarietybyanorderofmagnitude.Importantly,this larlysuitedforVQAtasks. WefeedthismodelQ,I inputs
is not only due to dataset size: while Encyclopedic-VQA foreachtestsampletoproducemodelanswers.
andKVQA[47]arethelargestdatasetsandhaveacompa- Tab.5(firstrow)showsthatPaLIhasanaccuracyofonly
rable number of questions, KVQA has much less variety. 13.0%. This low performance indicates that PaLI either
In terms of disparity, our dataset is close to the best ones fails to recognise fine-grained categories and instances, or
OK-VQA[37]andA-OKVQA[46],andbetterthanFVQA, failstoprovidedetailedproperties,orboth. Henceencyclo-
| KVQAandS3VQA. |     |     |     |     |     |     | pedicknowledgeistrulydifficultforthismodel. |     |     |     |     |     |     |     |     |
| ------------- | --- | --- | --- | --- | --- | --- | ------------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- |

Model
|     | System       |     | Retrieval | Extrainput |     | PaLI[14] | PaLM[17] | GPT-3[10] |     |
| --- | ------------ | --- | --------- | ---------- | --- | -------- | -------- | --------- | --- |
|     | Vanillamodel |     | -         |            | -   | 13.0%    | 19.7%    | 15.5%     |     |
|     |              |     | SubjectC  |            | -   | 16.7%    | 31.0%    | 26.9%     |     |
|     | Oracle       |     | KBArticle |            | -   | 29.7%    | 78.4%    | 77.4%     |     |
|     |              |     | KBSection |            | -   | 48.8%    | 87.0%    | 82.1%     |     |
|     |              |     | KBArticle |            | -   | 21.4%    | 48.0%    | 44.9%     |     |
Lens
|     |     |     | KBSection |     | -   | 28.1% | 48.8% | 44.6% |     |
| --- | --- | --- | --------- | --- | --- | ----- | ----- | ----- | --- |
Captions
|     | PromptCap |     | -   |     |     | 17.8% | 29.7% | 25.6% |     |
| --- | --------- | --- | --- | --- | --- | ----- | ----- | ----- | --- |
NNtrainsamples
| Table 5: Accuracy | on single-hop | questions. |     |              |          |     |                |                             |           |
| ----------------- | ------------- | ---------- | --- | ------------ | -------- | --- | -------------- | --------------------------- | --------- |
|                   |               |            | We  | report model | accuracy | for | our single-hop | templated and automatically | generated |
questions. WhilelargemodelsstrugglewithourEncyclopedic-VQAdataset,ourexperimentsshowthepromiseofaugmentingthemwith
aretrievalmechanism.
PaLM and GPT-3. Models with a larger language un- trieval component shows great potential for predicting de-
derstanding component [11, 10, 17, 40] might be bet- tailedproperties,and(3)sincePaLMandGPT-3workmuch
ter at memorizing detailed properties and extracting infor- better than PaLI, having a strong language understanding
mation from textual knowledge base entries. Therefore, modelisimportantforextractingtherightinformationfrom
we experiment with PaLM 2 [17] (text-bison@001 thefree-formtextKBarticle.
model, available through the PaLM API) and GPT-3 [10] OracleretrievalofKBsection. Finally,tounderstandthe
(text-davinci-003, accessible through the OpenAI importanceofretrievinginformationthatismoreprecisely
API).Botharetrainedonmassiveamountsoftextincluding localizedthananentireWikipediapage, weprovidetothe
Wikipedia. GPT-3 is especially large, with 175B parame- modelstheground-truthsectionwhichsupportstheanswer
ters. Here we feed each model only the text questions Q (Oracle-KBSectioninTab.5). Again,accuracyimproves
as thery cannot consume images. This measures how well to48.8%forPaLI,87.0%forPaLMand82.1%forGPT-3.
our dataset can be solved by language alone. These mod- Thisdemonstratestheimportanceofprovidingexactinfor-
elsreachamodestaccuracy(19.7%,15.5%,Tab.5). While mationtothelanguagecomponent. Asabonus,thesmaller
they only takes textual inputs, they can be made multi- the retrieved document is upon which the model bases its
modal by adding a visual retrieval mechanism, as we do answer,themoreverifiableandinterpretablethisansweris.
inthefollowingsections.
5.3.LargeModelswithVisualRetrieval
5.2.LargeModelswithOracleRetrieval
Lens-basedretrievalofKBarticle. Togobeyondtheora-
Oracle retrieval of subject C. This experiment tests cledemonstrationabove,asproof-of-conceptweproposeto
whether the most difficult aspect of Encyclopedic-VQA is augmentlargemodelswitharealretrievalsystembasedon
recognizingfine-grainedcategoriesandinstancesintheim- GoogleLens[1]. GoogleLensisanimageretrievalsystem
age. Therefore we provide along with the test question its whichindexesahugeamountofwebimages.Givenaquery
corresponding subject category C as an additional textual image,itfindsotherimagesbasedontheirvisualsimilarity
inputtoourmodels(i.e.aspartoftheprompt;Oracle-Sub- andrelevancetoobjectsitrecognizesinthequeryimage. It
ject C in Tab. 5). While PaLI shows small improvements returnsthemostsimilarindexedimagesalongwithanentity
over its non-retrieval version, PaLM and GPT-3 improve prediction based on these top-ranked images. To augment
considerably to 31.0% and 26.9%, respectively. However, oursystem,wesendGoogleLensthequeryimageI toob-
thisisstillratherlowperformance. Weconcludethatdeter- tain its entity prediction. We then find the best matching
miningthefine-grainedcategoryorinstanceisonlypartof KB article for this entity in our knowledge base. Finally,
whatmakesourdatasetdifficult. wefeedtheretrievedKBarticleasprompttoourmodelsas
| OracleretrievalofKB | article. | Wenowgoa |     | stepfurther |     | inSec.5.2. |     |     |     |
| ------------------- | -------- | -------- | --- | ----------- | --- | ---------- | --- | --- | --- |
andprovidethefullground-truthWikipediaarticleaboutC Results are shown as Lens - KB Article in Tab. 5. All
tothemodelsintheprompt(Oracle,KBArticleinTab.5). models greatly outperform their non-retrieval augmented
This time, results increase dramatically: 29.7% accuracy versions. PaLMandGPT-3inparticularmorethandouble,
for PaLI, 78.4% for PaLM and 77.4% for GPT-3. This to48.0%and44.9%respectively.
demonstrates that (1) memorizing detailed properties is a Lens-basedretrievalofKBsection. GivenaretrievedKB
hard challenge for vanilla large VLMs, (2) adding a re- article, wenowaimtoselectthemostrelevantKBsection

|               |         |     | Accuracy  |             |     |             | 5.5.ComparisontoPromptCap |       |                     |        |             |         |           |         |
| ------------- | ------- | --- | --------- | ----------- | --- | ----------- | ------------------------- | ----- | ------------------- | ------ | ----------- | ------- | --------- | ------- |
|               |         |     | w/correct | w/incorrect |     | KBretrieval |                           |       |                     |        |             |         |           |         |
|               |         |     |           |             |     |             | We compare                |       | our retrieval-based |        |             | methods | to        | Prompt- |
| LensRetrieval | Overall |     | retrieval | retrieval   |     | accuracy    |                           |       |                     |        |             |         |           |         |
|               |         |     |           |             |     |             | Cap [25],                 | which | has                 | strong | performance |         | on OK-VQA |         |
| KBArticle     | 48.0%   |     | 77.7%     | 21.2%       |     | 47.4%       |                           |       |                     |        |             |         |           |         |
(Tab.4). PromptCapisasystemwithmultiplecomponents.
| KBSection | 48.8% |     | 82.3% | 20.7% |     | 45.6% |          |            |       |       |          |     |        |       |
| --------- | ----- | --- | ----- | ----- | --- | ----- | -------- | ---------- | ----- | ----- | -------- | --- | ------ | ----- |
|           |       |     |       |       |     |       | It has a | captioning | model | which | consumes |     | both I | and Q |
andgeneratesanimagecaptiontailoredtoanswertheques-
Table6:Attributionforretrieval-augmentedVLMs.Wereport
|     |     |     |     |     |     |     | tion Q. | This caption | is  | then | passed | to GPT-3 | as  | context |
| --- | --- | --- | --- | --- | --- | --- | ------- | ------------ | --- | ---- | ------ | -------- | --- | ------- |
modelaccuracyforPaLM[17]conditionalonretrievalresults.Re-
trievalsuccessratesaresimilarforarticlesandsections,butPaLM toanswerQ. Additionally,PromptCapperformsin-context
hasbetteraccuracywhenaugmentedwithsections. learning: for a given test question, it uses the CLIP em-
|     |     |     |     |     |     |     | beddings  | [39]                                      | of Q and | I to | find the | 32 nearest |     | training |
| --- | --- | --- | --- | --- | --- | --- | --------- | ----------------------------------------- | -------- | ---- | -------- | ---------- | --- | -------- |
|     |     |     |     |     |     |     | examples. | Thenitincludestheir32correspondingPrompt- |          |      |          |            |     |          |
Capcaptions,questionsandanswersintheGPT-3prompt.
| withinitbasedonQ. |     | TodosowequeryPaLMwithaspe- |     |     |     |     |     |     |     |     |     |     |     |     |
| ----------------- | --- | -------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
cialpromptfeedingoneKBsectionatatime,alongwithQ In[25]theunderlyingLLMwasGPT-3. Inthisexperiment
while asking ‘can the answer to this question be found in wealsoapplyPromptCapwithPaLMandPaLI.Resultsin
thistext?’. WeretainallsectionsforwhichPaLManswers Tab. 5 show that PromptCap indeed does better than using
‘Yes’ (usually only one), and consider their concatenation PaLI, PaLM, or GPT-3 alone. However, it still performs
|                                 |     |     |     |                       |     |     | substantially | worse | than | our retrieval-augmented |     |     | methods. |     |
| ------------------------------- | --- | --- | --- | --------------------- | --- | --- | ------------- | ----- | ---- | ----------------------- | --- | --- | -------- | --- |
| asthefinalretrieved‘KBsection’. |     |     |     | Finally,wefeedthisre- |     |     |               |       |      |                         |     |     |          |     |
trievedsectionintheprompttoallmodels(PaLI,PaLMor Thisfurtherconfirmsthehardchallengeourdatasetposes.
GPT-3)asinSec.5.2.
5.6.Multi-answerandtwo-hopquestions
| Results | are shown | as  | Lens - | KB Section | in  | Tab. 5. Per- |     |     |     |     |     |     |     |     |
| ------- | --------- | --- | ------ | ---------- | --- | ------------ | --- | --- | --- | --- | --- | --- | --- | --- |
formance is now even higher for PaLI (28.1%) and PaLM To evaluate multi-answer questions, we convert the
|          |         |       |      |          |            |          | model prediction            |     | into | a set | of strings | and  | compute | the     |
| -------- | ------- | ----- | ---- | -------- | ---------- | -------- | --------------------------- | --- | ---- | ----- | ---------- | ---- | ------- | ------- |
| (48.8%), | whereas | GPT-3 | does | not seem | to benefit | further. |                             |     |      |       |            |      |         |         |
|          |         |       |      |          |            |          | intersection-over-the-union |     |      | (IoU) | between    | this | set     | and the |
Thesenumbersareroughlyhalfwaybetweenversionswith-
outretrievalandwithoracleretrieval. Weconcludethatre- set of ground-truth answers. If IoU >= 0.5 then we con-
|     |     |     |     |     |     |     | sider that | prediction | as  | correct. | If not, | then | we use | BEM |
| --- | --- | --- | --- | --- | --- | --- | ---------- | ---------- | --- | -------- | ------- | ---- | ------ | --- |
trievalaugmentationworksinpractice,andthatourdataset
leavessignificantheadroomforfutureresearchonretrieval- to determine the equivalence of the prediction list string
|     |     |     |     |     |     |     | and the | ground-truth | list. | For | two-hop | questions, |     | we use |
| --- | --- | --- | --- | --- | --- | --- | ------- | ------------ | ----- | --- | ------- | ---------- | --- | ------ |
augmentedVLMs.
|            |            |     |     |          |        |          | BEM as | described | for | single-hop | templated |     | and automati- |     |
| ---------- | ---------- | --- | --- | -------- | ------ | -------- | ------ | --------- | --- | ---------- | --------- | --- | ------------- | --- |
| CLIP-based | retrieval. |     | KAT | [22] and | REVIVE | [34] are |        |           |     |            |           |     |               |     |
callygeneratedquestions.
| two retrieval-augmented |            |     | VQA     | systems    | which | use frozen  |      |           |     |         |           |         |     |       |
| ----------------------- | ---------- | --- | ------- | ---------- | ----- | ----------- | ---- | --------- | --- | ------- | --------- | ------- | --- | ----- |
|                         |            |     |         |            |       |             | PaLI | with Lens | KB  | Section | Retrieval | obtains | an  | accu- |
| CLIP [39]               | embeddings | to  | perform | retrieval. |       | In Appendix |      |           |     |         |           |         |     |       |
racyof9.2%formulti-answerquestionsand14.7%fortwo-
| C we explore | there | whether | KAT | and | REVIVE | could po- |                |     |            |      |      |            |     |           |
| ------------ | ----- | ------- | --- | --- | ------ | --------- | -------------- | --- | ---------- | ---- | ---- | ---------- | --- | --------- |
|              |       |         |     |     |        |           | hop questions. |     | Similarly, | PaLM | with | KB Section |     | retrieval |
tentiallysucceedonEncyclopedic-VQA.
|     |     |     |     |     |     |     | achieves   | 33.6%         | and 22.8% | for   | multi-answer |       | and | two-hop |
| --- | --- | --- | --- | --- | --- | --- | ---------- | ------------- | --------- | ----- | ------------ | ----- | --- | ------- |
|     |     |     |     |     |     |     | questions, | respectively. |           | GPT-3 | obtains      | 32.1% | for | multi-  |
answerand18.7%fortwo-hopquestions,inbetweenPaLI
5.4.AttributionforRetrieval-Augmentedmodels
andPaLM.Thesemodestperformances,especiallyontwo-
hopquestions,confirmthechallengeofourproposedtasks,
| The attribution |     | annotations |     | of our dataset |     | enable mea- |     |     |     |     |     |     |     |     |
| --------------- | --- | ----------- | --- | -------------- | --- | ----------- | --- | --- | --- | --- | --- | --- | --- | --- |
andhighlighttheusefulnessofEncyclopedic-VQAtomea-
suringwhetherretrieval-augmentedmodelsgivethecorrect
|                          |     |     |                             |     |     |     | sure progress | on  | designing | retrieval |     | mechanisms | for | such |
| ------------------------ | --- | --- | --------------------------- | --- | --- | --- | ------------- | --- | --------- | --------- | --- | ---------- | --- | ---- |
| answerfortherightreason. |     |     | Tab.6measuresattributionfor |     |     |     |               |     |           |           |     |            |     |      |
complextypesofquestions.
thePaLMmodelswithLensretrieval.
| First, | we observe | that | Google | Lens | retrieves | the cor- |     |     |     |     |     |     |     |     |
| ------ | ---------- | ---- | ------ | ---- | --------- | -------- | --- | --- | --- | --- | --- | --- | --- | --- |
6.Conclusions
| rect KB | Article 47.4% |     | of the | time. Furthermore, |     | we re- |     |     |     |     |     |     |     |     |
| ------- | ------------- | --- | ------ | ------------------ | --- | ------ | --- | --- | --- | --- | --- | --- | --- | --- |
port 45.6% retrieval accuracy at the finer level of a KB We introduced Encyclopedic-VQA, a large-scale VQA
Section. Thisdemonstratesthatgivenacorrectlyretrieved dataset about detailed properties of fine-grained categories
KBArticle,oursystemalmostalwaysfindsthecorrectSec- and instances, which includes a knowledge base with an-
tion within it. More importantly, if the retrieved KB Sec- swerattributions. Wedemonstratedthatourdatasetistruly
tionisincorrect,accuracydropsdrasticallyto20.7%,close difficultforstandardVLMs. Additionally,weshowedwith
tothenon-augmentedvariant(bothwhenusingPaLM).In both an oracle experiment and a prototype automatic sys-
contrast, if the correct KB Section is found, accuracy is tem that augmenting these models with a retrieval compo-
82.3%.Thus,retrievingthecorrectdocument,andevenbet- nent substantially improves results, yet leaving headroom
terthecorrectsection,isessentialtoperformingwellonour for even further improvements. Therefore our dataset en-
| dataset. |     |     |     |     |     |     | ablesfutureresearchonretrieval-augmentedVLMs. |     |     |     |     |     |     |     |
| -------- | --- | --- | --- | --- | --- | --- | --------------------------------------------- | --- | --- | --- | --- | --- | --- | --- |

References [12] Jannis Bulian, Christian Buck, Wojciech Gajewski, Ben-
|     |     |     |     |     |     |     | jamin | Boerschinger, |     | and Tal | Schuster. | Tomayto, |     | tomahto. |
| --- | --- | --- | --- | --- | --- | --- | ----- | ------------- | --- | ------- | --------- | -------- | --- | -------- |
[1] GoogleLens. https://lens.google.com-Webinterfaceavail- beyondtoken-levelanswerequivalenceforquestionanswer-
| ableathttps://images.google.com. |           |       |           | 8,13      |              |     |                |     |                                |     |     |     |     |          |
| -------------------------------- | --------- | ----- | --------- | --------- | ------------ | --- | -------------- | --- | ------------------------------ | --- | --- | --- | --- | -------- |
|                                  |           |       |           |           |              |     | ingevaluation. |     | arXivpreprintarXiv:2202.07654, |     |     |     |     | 2022. 7, |
| [2] Google                       | universal | image | embedding | challenge | leaderboard. |     |                |     |                                |     |     |     |     |          |
13
https://www.kaggle.com/competitions/google-universal- [13] Soravit Changpinyo, Doron Kukliansky, Idan Szpektor, Xi
image-embedding/leaderboard. 14 Chen, NanDing, andRaduSoricut. Allyoumayneedfor
[3] Huggingface. https://huggingface.co/sentence- VQAareimagecaptions. InNAACL,2022. 4,5
transformers/multi-qa-MiniLM-L6-cos-v1. 7 [14] XiChen,XiaoWang,SoravitChangpinyo,AJPiergiovanni,
[4] inat challenge 2021 - fgvc8 - leaderboard. Piotr Padlewski, Daniel Salz, Sebastian Goodman, Adam
https://www.kaggle.com/competitions/inaturalist- Grycner,BasilMustafa,LucasBeyer,AlexanderKolesnikov,
|                   |     |     |     |     |     |     | Joan | Puigcerver, | Nan | Ding, | Keran | Rong, | Hassan | Akbari, |
| ----------------- | --- | --- | --- | --- | --- | --- | ---- | ----------- | --- | ----- | ----- | ----- | ------ | ------- |
| 2021/leaderboard. |     | 14  |     |     |     |     |      |             |     |       |       |       |        |         |
[5] WikimediaCommons. https://commons.wikimedia.org. 4 GauravMishra,LintingXue,AshishThapliyal,JamesBrad-
|     |     |     |     |     |     |     | bury, | Weicheng | Kuo, | Mojtaba | Seyedhosseini, |     |     | Chao Jia, |
| --- | --- | --- | --- | --- | --- | --- | ----- | -------- | ---- | ------- | -------------- | --- | --- | --------- |
[6] ArmenAghajanyan,BernieHuang,CandaceRoss,Vladimir
|     |     |     |     |     |     |     | Burcu | Karagol | Ayan, | Carlos | Riquelme, |     | Andreas | Steiner, |
| --- | --- | --- | --- | --- | --- | --- | ----- | ------- | ----- | ------ | --------- | --- | ------- | -------- |
Karpukhin,HuXu,NamanGoyal,DmytroOkhonko,Man-
|     |     |     |     |     |     |     | Anelia | Angelova, | Xiaohua |     | Zhai, Neil | Houlsby, |     | and Radu |
| --- | --- | --- | --- | --- | --- | --- | ------ | --------- | ------- | --- | ---------- | -------- | --- | -------- |
darJoshi,GargiGhosh,MikeLewis,andLukeZettlemoyer.
|     |     |     |     |     |     |     | Soricut. | PaLI: | A Jointly-Scaled |     |     | Multilingual | Language- |     |
| --- | --- | --- | --- | --- | --- | --- | -------- | ----- | ---------------- | --- | --- | ------------ | --------- | --- |
CM3:Acausalmaskedmultimodalmodeloftheinternet.In
|                   |              |            |               |               |             |           | ImageModel.                   |           | arXiv,2022.  | 1,2,3,7,8  |                |           |          |           |
| ----------------- | ------------ | ---------- | ------------- | ------------- | ----------- | --------- | ----------------------------- | --------- | ------------ | ---------- | -------------- | --------- | -------- | --------- |
| arXiv,2022.       |              | 3          |               |               |             |           |                               |           |              |            |                |           |          |           |
|                   |              |            |               |               |             |           | [15] Yang                     | Chen,     | Hexiang      | Hu, Yi     | Luan,          | Haitian   | Sun,     | Soravit   |
| [7] Jean-Baptiste |              | Alayrac,   | Jeff Donahue, |               | Pauline     | Luc, An-  |                               |           |              |            |                |           |          |           |
|                   |              |            |               |               |             |           | Changpinyo,                   |           | Alan Ritter, |            | and Ming-Wei   |           | Chang.   | Can       |
| toine             | Miech,       | Iain Barr, | Yana          | Hasson, Karel | Lenc,       | Arthur    |                               |           |              |            |                |           |          |           |
|                   |              |            |               |               |             |           | pre-trained                   |           | vision and   | language   |                | models    | answer   | visual    |
| Mensch,           | Katie        | Millican,  | Malcolm       | Reynolds,     | Roman       | Ring,     |                               |           |              |            |                |           |          |           |
|                   |              |            |               |               |             |           | information-seekingquestions? |           |              |            | InarXiv,2023.  |           | 4        |           |
| Eliza             | Rutherford,  | Serkan     | Cabi,         | Tengda Han,   | Zhitao      | Gong,     |                               |           |              |            |                |           |          |           |
|                   |              |            |               |               |             |           | [16] Yen-Chun                 | Chen,     | Linjie       | Li,        | Licheng        | Yu, Ahmed |          | El Kholy, |
| Sina              | Samangooei,  | Marianne   | Monteiro,     | Jacob         | Menick,     | Se-       |                               |           |              |            |                |           |          |           |
|                   |              |            |               |               |             |           | Faisal                        | Ahmed,    | Zhe          | Gan,       | Yu Cheng,      | and       | Jingjing | Liu.      |
| bastian           | Borgeaud,    | Andrew     | Brock,        | Aida          | Nematzadeh, | Sa-       |                               |           |              |            |                |           |          |           |
|                   |              |            |               |               |             |           | UNITER:                       | UNiversal |              | Image-TExt | Representation |           |          | Learning. |
| hand              | Sharifzadeh, | Mikolaj    | Binkowski,    |               | Ricardo     | Barreira, |                               |           |              |            |                |           |          |           |
|                   |              |            |               |               |             |           | InECCV,2020.                  |           | 4            |            |                |           |          |           |
| Oriol             | Vinyals,     | Andrew     | Zisserman,    | and           | Karen       | Simonyan. |                               |           |              |            |                |           |          |           |
|                   |              |            |               |               |             |           | [17] Aakanksha                |           | Chowdhery,   | Sharan     |                | Narang,   | Jacob    | Devlin,   |
Flamingo:aVisualLanguageModelforFew-ShotLearning.
|               |        |           |          |        |     |          | Maarten | Bosma,    | Gaurav |        | Mishra, | Adam   | Roberts, | Paul   |
| ------------- | ------ | --------- | -------- | ------ | --- | -------- | ------- | --------- | ------ | ------ | ------- | ------ | -------- | ------ |
| NeurIPS,2022. |        | 1,2,3     |          |        |     |          |         |           |        |        |         |        |          |        |
|               |        |           |          |        |     |          | Barham, | Hyung     | Won    | Chung, | Charles |        | Sutton,  | Sebas- |
| [8] Stanislaw | Antol, | Aishwarya | Agrawal, | Jiasen | Lu, | Margaret |         |           |        |        |         |        |          |        |
|               |        |           |          |        |     |          | tian    | Gehrmann, | Parker |        | Schuh,  | Kensen | Shi,     | Sasha  |
Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker
Parikh. VQA: visual question answering. In ICCV, 2015. Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran,
3,13
|               |           |        |     |         |        |           | EmilyReif, |       | NanDu,  | BenHutchinson, |         | ReinerPope, |     | James    |
| ------------- | --------- | ------ | --- | ------- | ------ | --------- | ---------- | ----- | ------- | -------------- | ------- | ----------- | --- | -------- |
| [9] Sebastian | Borgeaud, | Arthur |     | Mensch, | Jordan | Hoffmann, |            |       |         |                |         |             |     |          |
|               |           |        |     |         |        |           | Bradbury,  | Jacob | Austin, |                | Michael | Isard,      | Guy | Gur-Ari, |
TrevorCai,ElizaRutherford,KatieMillican,Georgevanden Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay
Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier
Clark,DiegodeLasCasas,AureliaGuy,JacobMenick,Ro- Garcia,VedantMisra,KevinRobinson,LiamFedus,Denny
manRing,TomHennigan,SaffronHuang,LorenMaggiore,
|     |     |     |     |     |     |     | Zhou, | Daphne | Ippolito, | David | Luan, |     | Hyeontaek | Lim, |
| --- | --- | --- | --- | --- | --- | --- | ----- | ------ | --------- | ----- | ----- | --- | --------- | ---- |
ChrisJones,AlbinCassirer,AndyBrock,MichelaPaganini,
|     |     |     |     |     |     |     | Barret | Zoph, | Alexander | Spiridonov, |     | Ryan | Sepassi, | David |
| --- | --- | --- | --- | --- | --- | --- | ------ | ----- | --------- | ----------- | --- | ---- | -------- | ----- |
GeoffreyIrving, OriolVinyals, SimonOsindero, KarenSi- Dohan, Shivani Agrawal, Mark Omernick, Andrew M.
monyan,JackW.Rae,ErichElsen,andLaurentSifre. Im- Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat,
provinglanguagemodelsbyretrievingfromtrillionsofto- AitorLewkowycz, EricaMoreira, RewonChild, Oleksandr
| kens. | arXiv:2112.04426,2022. |     |     | 1,4 |     |     |          |           |     |              |     |       |        |       |
| ----- | ---------------------- | --- | --- | --- | --- | --- | -------- | --------- | --- | ------------ | --- | ----- | ------ | ----- |
|       |                        |     |     |     |     |     | Polozov, | Katherine |     | Lee, Zongwei |     | Zhou, | Xuezhi | Wang, |
[10] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Sub- Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta,
biah,JaredDKaplan,PrafullaDhariwal,ArvindNeelakan- JasonWei,KathyMeier-Hellstern,DouglasEck,JeffDean,
tan,PranavShyam,GirishSastry,AmandaAskell,etal.Lan- Slav Petrov, and Noah Fiedel. PaLM: Scaling Language
guagemodelsarefew-shotlearners. Advancesinneuralin- ModelingwithPathways. arXiv,2022. 5,7,8,9,14
formationprocessingsystems,33:1877–1901,2020.
|     |     |     |     |     |     | 7,8 | [18] Hyung | Won | Chung, | Le Hou, | Shayne |     | Longpre, | Barret |
| --- | --- | --- | --- | --- | --- | --- | ---------- | --- | ------ | ------- | ------ | --- | -------- | ------ |
[11] TomB.Brown,BenjaminMann,NickRyder,MelanieSub- Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang,
biah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakan- Mostafa Dehghani, Siddhartha Brahma, et al. Scaling
tan, Pranav Shyam, Girish Sastry, Amanda Askell, Sand- instruction-finetuned language models. arXiv preprint
|               |     |                     |     |          |          |     | arXiv:2210.11416,2022. |     |     | 5,14 |     |     |     |     |
| ------------- | --- | ------------------- | --- | -------- | -------- | --- | ---------------------- | --- | --- | ---- | --- | --- | --- | --- |
| hini Agarwal, |     | Ariel Herbert-Voss, |     | Gretchen | Krueger, | Tom |                        |     |     |      |     |     |     |     |
Henighan,RewonChild,AdityaRamesh,DanielM.Ziegler, [19] Matthew Dunn, Levent Sagun, Mike Higgins, V. Ugur
JeffreyWu,ClemensWinter,ChristopherHesse,MarkChen, Guney, Volkan Cirik, and Kyunghyun Cho. SearchQA: A
Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, newQ&Adatasetaugmentedwithcontextfromasearchen-
JackClark,ChristopherBerner,SamMcCandlish,AlecRad- gine. InarXiv,2017. 4
ford,IlyaSutskever,andDarioAmodei. LanguageModels [20] HaoyuanGao, JunhuaMao, JieZhou, ZhihengHuang, Lei
areFew-ShotLearners. arXiv,2020. 8 Wang,andWeiXu. Areyoutalkingtoamachine? Dataset

andmethodsformultilingualimagequestion. InNeurIPS, [34] YuanzeLin,YujiaXie,DongdongChen,YichongXu,Chen-
| 2015. | 3   |     |     |     |     |     | guangZhu,andLuYuan. |     |                 | Revive:Regionalvisualrepresen- |                 |         |
| ----- | --- | --- | --- | --- | --- | --- | ------------------- | --- | --------------- | ------------------------------ | --------------- | ------- |
|       |     |     |     |     |     |     | tation matters      | in  | knowledge-based |                                | visual question | answer- |
[21] YashGoyal,TejasKhot,DouglasSummers-Stay,DhruvBa-
tra, and Devi Parikh. Making the V in VQA matter: Ele- ing. arXivpreprintarXiv:2206.01201,2022. 9,13,14
vating the role of image understanding in Visual Question [35] Mateusz Malinowski and Mario Fritz. A multi-world ap-
Answering. InCVPR,2017. 3,7,13 proachtoquestionansweringaboutreal-worldscenesbased
InNeurIPS,2014.
[22] Liangke Gui, Borui Wang, Qiuyuan Huang, Alex Haupt- onuncertaininput. 3
mann, Yonatan Bisk, and Jianfeng Gao. KAT: A Knowl- [36] KennethMarino,XinleiChen,DeviParikh,AbhinavGupta,
edgeAugmentedTransformerforVision-and-Language. In and Marcus Rohrbach. KRISP: Integrating Implicit and
NAACL,2022. 1,4,7,9,13,14 Symbolic Knowledge for Open-Domain Knowledge-Based
[23] KelvinGuu,KentonLee,ZoraTung,PanupongPasupat,and VQA. InProc.CVPR,2021. 1,4
Ming-WeiChang.REALM:Retrieval-AugmentedLanguage [37] Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and
ModelPre-Training. InProc.ICML,2020. 1,4 RoozbehMottaghi. OK-VQA:Avisualquestionanswering
| [24] Yaru                            | Hao, | Haoyu Song, | Li  | Dong, Shaohan | Huang, | Zewen    |            |           |          |            |     |             |
| ------------------------------------ | ---- | ----------- | --- | ------------- | ------ | -------- | ---------- | --------- | -------- | ---------- | --- | ----------- |
|                                      |      |             |     |               |        |          | benchmark  | requiring | external | knowledge. | In  | CVPR, 2019. |
| Chi,WenhuiWang,ShumingMa,andFuruWei. |      |             |     |               |        | Language | 1,2,3,7,13 |           |          |            |     |             |
modelsaregeneral-purposeinterfaces. InarXiv,2022. 1,2, [38] TodorMihaylov,PeterClark,TusharKhot,andAshishSab-
3
|     |     |     |     |     |     |     | harwal. | Can a | suit of armor | conduct | electricity? | a new |
| --- | --- | --- | --- | --- | --- | --- | ------- | ----- | ------------- | ------- | ------------ | ----- |
[25] YushiHu,HangHua,ZhengyuanYang,WeijiaShi,NoahA
datasetforopenbookquestionanswering.InEMNLP,2018.
| Smith,                | and | Jiebo Luo. | Promptcap:                     | Prompt-guided |       | task-      | 4                  |         |               |          |                |                |
| --------------------- | --- | ---------- | ------------------------------ | ------------- | ----- | ---------- | ------------------ | ------- | ------------- | -------- | -------------- | -------------- |
| awareimagecaptioning. |     |            | arXivpreprintarXiv:2211.09699, |               |       |            |                    |         |               |          |                |                |
|                       |     |            |                                |               |       |            | [39] Alec Radford, |         | Jong Wook     | Kim,     | Chris Hallacy, | Aditya         |
| 2022.                 | 7,9 |            |                                |               |       |            |                    |         |               |          |                |                |
|                       |     |            |                                |               |       |            | Ramesh,            | Gabriel | Goh, Sandhini |          | Agarwal,       | Girish Sastry, |
| [26] Ziniu            | Hu, | Ahmet      | Iscen,                         | Chen Sun,     | Zirui | Wang, Kai- |                    |         |               |          |                |                |
|                       |     |            |                                |               |       |            | Amanda             | Askell, | Pamela        | Mishkin, | Jack Clark,    | Gretchen       |
Wei Chang, Yizhou Sun, Cordelia Schmid, David A. Krueger, and Ilya Sutskever. Learning transferable visual
Ross, and Alireza Fathi. REVEAL: Retrieval-Augmented modelsfromnaturallanguagesupervision. InICML,2021.
| Visual-Language |     | Pre-Training |     | with Multi-Source |     | Multi- |     |     |     |     |     |     |
| --------------- | --- | ------------ | --- | ----------------- | --- | ------ | --- | --- | --- | --- | --- | --- |
3,9,13
| modalKnowledgeMemory. |     |     |     | InCVPR,2023. | 1,4,7 |     |                 |                    |     |     |            |             |
| --------------------- | --- | --- | --- | ------------ | ----- | --- | --------------- | ------------------ | --- | --- | ---------- | ----------- |
|                       |     |     |     |              |       |     | [40] JackW.Rae, | SebastianBorgeaud, |     |     | TrevorCai, | KatieMilli- |
[27] Aman Jain, Mayank Kothyari, Vishwajeet Kumar, Preethi can,JordanHoffmann,FrancisSong,JohnAslanides,Sarah
Jyothi,GaneshRamakrishnan,andSoumenChakrabarti.Se- Henderson, Roman Ring, Susannah Young, Eliza Ruther-
| lect, | substitute, | search: | A   | new benchmark | for | knowledge- |     |     |     |     |     |     |
| ----- | ----------- | ------- | --- | ------------- | --- | ---------- | --- | --- | --- | --- | --- | --- |
ford,TomHennigan,JacobMenick,AlbinCassirer,Richard
| augmentedvisualquestionanswering. |     |     |     | InSIGIR,2021. |     | 2,3, |                |     |                    |     |           |            |
| --------------------------------- | --- | --- | --- | ------------- | --- | ---- | -------------- | --- | ------------------ | --- | --------- | ---------- |
|                                   |     |     |     |               |     |      | Powell, George |     | van den Driessche, |     | Lisa Anne | Hendricks, |
4,5,7
|     |     |     |     |     |     |     | Maribeth | Rauh, | Po-Sen Huang, |     | Amelia Glaese, | Johannes |
| --- | --- | --- | --- | --- | --- | --- | -------- | ----- | ------------- | --- | -------------- | -------- |
[28] ChaoJia,YinfeiYang,YeXia,Yi-TingChen,ZaranaParekh, Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Ue-
HieuPham,QuocV.Le,Yun-HsuanSung,ZhenLi,andTom sato, John Mellor, Irina Higgins, Antonia Creswell, Nat
Duerig.Scalingupvisualandvision-languagerepresentation
|                                   |     |     |     |              |     |     | McAleese, | Amy | Wu, Erich | Elsen, | Siddhant | Jayakumar, |
| --------------------------------- | --- | --- | --- | ------------ | --- | --- | --------- | --- | --------- | ------ | -------- | ---------- |
| learningwithnoisytextsupervision. |     |     |     | InICML,2021. |     | 3   |           |     |           |        |          |            |
ElenaBuchatskaya,DavidBudden,EsmeSutherland,Karen
[29] MandarJoshi,EunsolChoi,DanielS.Weld,andLukeZettle- Simonyan, MichelaPaganini, LaurentSifre, LenaMartens,
moyer. TriviaQA: A large scale distantly supervised chal- Xiang Lorraine Li, Adhiguna Kuncoro, Aida Nematzadeh,
lengedatasetforreadingcomprehension. InACL,2017. 4 Elena Gribovskaya, Domenic Donato, Angeliki Lazaridou,
[30] UrvashiKhandelwal,OmerLevy,DanJurafsky,LukeZettle- ArthurMensch,Jean-BaptisteLespiau,MariaTsimpoukelli,
moyer,andMikeLewis. Generalizationthroughmemoriza- NikolaiGrigorev,DougFritz,ThibaultSottiaux,MantasPa-
tion: Nearestneighborlanguagemodels. InICLR,2020. 1, jarskas, Toby Pohlen, Zhitao Gong, Daniel Toyama, Cy-
| 4   |     |     |     |     |     |     | priendeMassond’Autume,YujiaLi,TayfunTerzi,Vladimir |     |     |     |     |     |
| --- | --- | --- | --- | --- | --- | --- | -------------------------------------------------- | --- | --- | --- | --- | --- |
Mikulik,IgorBabuschkin,AidanClark,DiegodeLasCasas,
| [31] Tushar | Khot, | Peter | Clark, | Michal Guerquin, | Peter | Jansen, |     |     |     |     |     |     |
| ----------- | ----- | ----- | ------ | ---------------- | ----- | ------- | --- | --- | --- | --- | --- | --- |
and Ashish Sabharwal. QASC: A dataset for question an- Aurelia Guy, Chris Jones, James Bradbury, Matthew John-
sweringviasentencecomposition. InAAAI,2020. 4 son, Blake Hechtman, Laura Weidinger, Iason Gabriel,
[32] Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Ui- WilliamIsaac,EdLockhart,SimonOsindero,LauraRimell,
|     |     |     |     |     |     |     | Chris Dyer, | Oriol | Vinyals, | Kareem | Ayoub, | Jeff Stanway, |
| --- | --- | --- | --- | --- | --- | --- | ----------- | ----- | -------- | ------ | ------ | ------------- |
jlings,IvanKrasin,JordiPont-Tuset,ShahabKamali,Stefan
|                         |        |          |     |                             |          |          | Lorrayne            | Bennett, | Demis   | Hassabis, | Koray   | Kavukcuoglu, |
| ----------------------- | ------ | -------- | --- | --------------------------- | -------- | -------- | ------------------- | -------- | ------- | --------- | ------- | ------------ |
| Popov,                  | Matteo | Malloci, | Tom | Duerig, and                 | Vittorio | Ferrari. |                     |          |         |           |         |              |
|                         |        |          |     |                             |          |          | and GeoffreyIrving. |          | Scaling | Language  | Models: | Methods,     |
| TheOpenImagesDatasetV4: |        |          |     | Unifiedimageclassification, |          |          |                     |          |         |           |         |              |
object detection, and visual relationship detection at scale. Analysis&InsightsfromTrainingGopher. arXiv,2022. 8
IJCV,2020. 4 [41] ColinRaffel,NoamShazeer,AdamRoberts,KatherineLee,
SharanNarang,MichaelMatena,YanqiZhou,WeiLi,,and
| [33] Patrick | Lewis, | Ethan | Perez, | Aleksandra | Piktus, | Fabio |     |     |     |     |     |     |
| ------------ | ------ | ----- | ------ | ---------- | ------- | ----- | --- | --- | --- | --- | --- | --- |
Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich PeterJLiu. Exploringthelimitsoftransferlearningwitha
Ku¨ttler, Mike Lewis, Wen tau Yih, Tim Rockta¨schel, Se- unifiedtext-to-texttransformer. JMLR,2021. 13
bastian Riedel, and Douwe Kiela. Retrieval-Augmented [42] Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and
Generation for Knowledge-Intensive NLP Tasks. In Proc. PercyLiang.SQuAD:100,000+questionsformachinecom-
| NeurIPS,2020. |     | 1,4 |     |     |     |     | prehensionoftext. |     | InEMNLP,2016. |     | 4   |     |
| ------------- | --- | --- | --- | --- | --- | --- | ----------------- | --- | ------------- | --- | --- | --- |

[43] NilsReimersandIrynaGurevych. Sentence-bert: Sentence [60] Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella,
embeddingsusingsiamesebert-networks. InEMNLP,2019. Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang,
| 7   |     |     |     |     |     |     |     | BoxinLi,ChunyuanLi,CeLiu,MengchenLiu,ZichengLiu, |     |
| --- | --- | --- | --- | --- | --- | --- | --- | ------------------------------------------------ | --- |
[44] MengyeRen,RyanKiros,andRichardS.Zemel. Exploring YumaoLu,YuShi,LijuanWang,JianfengWang,BinXiao,
modelsanddataforimagequestionanswering. InNeurIPS, ZhenXiao,JianweiYang,MichaelZeng,LuoweiZhou,and
| 2015.        | 4      |            |     |                    |     |     |            | PengchuanZhang. | Florence: ANewFoundationModelfor |
| ------------ | ------ | ---------- | --- | ------------------ | --- | --- | ---------- | --------------- | -------------------------------- |
|              |        |            |     |                    |     |     |            | ComputerVision. | arXiv,2021. 3                    |
| [45] Eleanor | Rosch. | Principles |     | of categorization. |     |     | In Eleanor |                 |                                  |
RoschandB.B.Lloyd,editors,CognitionandCategoriza-
| tion,pages27–48.LawrenceErlbaumAssociates,1978. |          |        |         |             |     |             | 2      |     |     |
| ----------------------------------------------- | -------- | ------ | ------- | ----------- | --- | ----------- | ------ | --- | --- |
| [46] Dustin                                     | Schwenk, | Apoorv |         | Khandelwal, |     | Christopher | Clark, |     |     |
| Kenneth                                         | Marino,  | and    | Roozbeh | Mottaghi.   |     | A-OKVQA:    | A      |     |     |
benchmarkforvisualquestionansweringusingworldknowl-
| edge.                    | InECCV,2022. |                                         | 2,3,7,13     |              |                      |         |           |     |     |
| ------------------------ | ------------ | --------------------------------------- | ------------ | ------------ | -------------------- | ------- | --------- | --- | --- |
| [47] Sanket              | Shah,        | Anand                                   | Mishra,      |              | Naganand             | Yadati, | and       |     |     |
| Partha                   | Pratim       | Talukdar.                               | KVQA:        |              | Knowledge-aware      |         | visual    |     |     |
| questionanswering.       |              |                                         | InAAAI,2019. |              | 2,3,7                |         |           |     |     |
| [48] Krishna             | Srinivasan,  |                                         | Karthik      | Raman,       | Jiecao               | Chen,   | Michael   |     |     |
| Bendersky,andMarcNajork. |              |                                         |              | Wit:         | Wikipedia-basedimage |         |           |     |     |
| text                     | dataset      | for multimodal                          |              | multilingual |                      | machine | learning. |     |     |
| SIGIR,2021.              |              | 3,4                                     |              |              |                      |         |           |     |     |
| [49] AndyStirling.       |              | Ageneralframeworkforanalysisngdiversity |              |              |                      |         |           |     |     |
| in science,              |              | technology,                             | and          | society.     | Journal              | of      | the Royal |     |     |
| SocietyInterface,2007.   |              |                                         | 7            |              |                      |         |           |     |     |
[50] AlonTalmorandJonathanBerant.Thewebasaknowledge-
| baseforansweringcomplexquestions. |     |     |     |     | InNAACL,2018. |     | 4   |     |     |
| --------------------------------- | --- | --- | --- | --- | ------------- | --- | --- | --- | --- |
[51] GrantvanHorn,ElijahCole,SaraBeery,KimberlyWilber,
| SergeBelongie,andOisinMacAodha. |     |          |             |       | BenchmarkingRep- |              |     |     |     |
| ------------------------------- | --- | -------- | ----------- | ----- | ---------------- | ------------ | --- | --- | --- |
| resentation                     |     | Learning | for Natural | World | Image            | Collections. |     |     |     |
| InCVPR,2021.                    |     | 2,4      |             |       |                  |              |     |     |     |
[52] PeterVickers,NikolaosAletras,EmilioMonti,andLoicBar-
| rault.                      | InFactuality: |     | EfficientIntegrationofRelevantFacts |             |     |     |     |     |     |
| --------------------------- | ------------- | --- | ----------------------------------- | ----------- | --- | --- | --- | --- | --- |
| forVisualQuestionAnswering. |               |     |                                     | InACL,2021. |     | 1,4 |     |     |     |
[53] PengWang,QiWu,ChunhuaShen,AnthonyDick,andAn-
| tonvandenHengel. |                        |        | FVQA:Fact-basedvisualquestionan- |      |             |     |         |     |     |
| ---------------- | ---------------------- | ------ | -------------------------------- | ---- | ----------- | --- | ------- | --- | --- |
| swering.         | IEEETrans.onPAMI,2018. |        |                                  |      | 3,7         |     |         |     |     |
| [54] Jason       | Wei,                   | Xuezhi | Wang,                            | Dale | Schuurmans, |     | Maarten |     |     |
Bosma,BrianIchter,FeiXia,EdChi,QuocLe,andDenny
| Zhou.                | Chain-of-Thought |        |                 | Prompting | Elicits | Reasoning | in      |     |     |
| -------------------- | ---------------- | ------ | --------------- | --------- | ------- | --------- | ------- | --- | --- |
| LargeLanguageModels. |                  |        | InNeurIPS,2022. |           |         | 14        |         |     |     |
| [55] Johannes        | Welbl,           | Pontus | Stenetorp,      |           | and     | Sebastian | Riedel. |     |     |
Constructingdatasetsformulti-hopreadingcomprehension
| acrossdocuments. |     |            | InACL,2018. |          | 4       |        |       |     |     |
| ---------------- | --- | ---------- | ----------- | -------- | ------- | ------ | ----- | --- | --- |
| [56] T. Weyand,  |     | A. Araujo, | B.          | Cao, and | J. Sim. | Google | land- |     |     |
marksdatasetv2-alarge-scalebenchmarkforinstance-level
| recognitionandretrieval. |     |     | InCVPR,2020. |     |     | 2,4 |     |     |     |
| ------------------------ | --- | --- | ------------ | --- | --- | --- | --- | --- | --- |
[57] ZhengyuanYang,ZheGan,JianfengWang,XiaoweiHu,Yu-
| maoLu,ZichengLiu,andLijuanWang.       |     |     |     |     |     | Anempiricalstudy |     |     |     |
| ------------------------------------- | --- | --- | --- | --- | --- | ---------------- | --- | --- | --- |
| ofgpt-3forfew-shotknowledge-basedvqa. |     |     |     |     |     | InAAAI,2022.     |     |     |     |
1,2,3
| [58] Zhilin                     | Yang,  | Peng         | Qi, Saizheng   |               | Zhang,       | Yoshua      | Bengio,     |     |     |
| ------------------------------- | ------ | ------------ | -------------- | ------------- | ------------ | ----------- | ----------- | --- | --- |
| William                         | Cohen, | Ruslan       | Salakhutdinov, |               | and          | Christopher | D.          |     |     |
| Manning.                        |        | HotpotQA:    | A              | dataset       | for diverse, |             | explainable |     |     |
| multi-hopquestionanswering.     |        |              |                | InEMNLP,2018. |              |             | 3,4,6       |     |     |
| [59] Licheng                    | Yu,    | Eunbyung     | Park,          | Alexander     |              | C.          | Berg, and   |     |     |
| Tamara                          | L.     | Berg. Visual | madlibs:       |               | Fill in      | the blank   | image       |     |     |
| generationandquestionanswering. |        |              |                |               | InICCV,2015. |             | 3           |     |     |

A.Correctnessofourdataset astrictevaluationmeasureandbecausetheirquestionsare
|         |     |        |           |         |         |           | more           | open-ended |     | (they   | are not supported |           | by a controlled |         |
| ------- | --- | ------ | --------- | ------- | ------- | --------- | -------------- | ---------- | --- | ------- | ----------------- | --------- | --------------- | ------- |
| We rely | on  | iNat21 | and GLDv2 | to have | clearly | identifi- |                |            |     |         |                   |           |                 |         |
|         |     |        |           |         |         |           | knowledgebase, |            |     | Sec.4), | thosedatasets     | collect10 |                 | ground- |
ablecategoriesonthetestimagesofourdataset,andtoob-
truthanswersperquestion,tocoversomeofthevariability
taintheirground-truthlabels.Furthermore,weonlyusecat- in answer formulation. This then enables a more relaxed
egorieswhichunambiguouslymaptoWikipediaarticlesby
|                                                  |     |     |     |     |     |     | evaluation, |                 | as the | model | answer need    | only | match         | some of |
| ------------------------------------------------ | --- | --- | --- | --- | --- | --- | ----------- | --------------- | ------ | ----- | -------------- | ---- | ------------- | ------- |
| explicitlyseekingforone-to-onemappings(Sec.4.1). |     |     |     |     |     | The |             |                 |        |       |                |      |               |         |
|                                                  |     |     |     |     |     |     | the         | 10 ground-truth |        | ones  | (three matches |      | for a perfect | an-     |
mainremainingpossiblesourceoferroriswhetheraques-
swer). Nevertheless,itisinstructivetoverifywhetherexact
| tion can | be answered |     | given | the corresponding |     | Wikipedia |     |     |     |     |     |     |     |     |
| -------- | ----------- | --- | ----- | ----------------- | --- | --------- | --- | --- | --- | --- | --- | --- | --- | --- |
matchingwouldworkonourdataset,wherewehaveasin-
| article, | and whether |       | the recorded | ground-truth   |     | answer is |     |              |     |         |          |          |          |        |
| -------- | ----------- | ----- | ------------ | -------------- | --- | --------- | --- | ------------ | --- | ------- | -------- | -------- | -------- | ------ |
|          |             |       |              |                |     |           | gle | ground-truth |     | answer. | We start | from the | publicly | avail- |
| indeed   | correct.    | Human | annotators   | systematically |     | vali-     |     |              |     |         |          |          |          |        |
ableexactmatchingimplementationof[22]andincludead-
| date these       | aspects | for                               | every | single question+answer |     | pair |                                              |     |           |       |          |        |             |     |
| ---------------- | ------- | --------------------------------- | ----- | ---------------------- | --- | ---- | -------------------------------------------- | --- | --------- | ----- | -------- | ------ | ----------- | --- |
|                  |         |                                   |       |                        |     |      | ditionalrelaxationsfornumbercomparisons(e.g. |     |           |       |          |        | numbers     |     |
| (Sec.4.2and4.3). |         | Yet,weperformhereanadditionaluser |       |                        |     |      |                                              |     |           |       |          |        |             |     |
|                  |         |                                   |       |                        |     |      | over                                         | 100 | - usually | years | - may be | off by | one, ranges | are |
studytogetannumericalestimateofqualityinthissense.
|     |     |     |     |     |     |     | correct | if  | they | partially | overlap, etc). | We  | find that | exact |
| --- | --- | --- | --- | --- | --- | --- | ------- | --- | ---- | --------- | -------------- | --- | --------- | ----- |
In this study, we randomly sample 100 questions from matchjudgementsareequivalenttohumanjudgementsonly
thetestset.Thenweasksixexpertstoeachanswer50ques-
|     |     |     |     |     |     |     | in  | 68% of | the cases. | Where | they | differ, exact | match | is al- |
| --- | --- | --- | --- | --- | --- | --- | --- | ------ | ---------- | ----- | ---- | ------------- | ----- | ------ |
tionseach,giventhecorrespondingWikipediapageasref-
|     |     |     |     |     |     |     | ways | overly | strict, | rejecting | answers | that | a human | would |
| --- | --- | --- | --- | --- | --- | --- | ---- | ------ | ------- | --------- | ------- | ---- | ------- | ----- |
erence. Theseexpertwerenotinvolvedintheoriginaldata judge as correct. This demonstrates that exact matching
collectionprocess.Thisprocessresultsinthreeanswersper
doesnotworkwellforourdataset,andjustifiesourchoice
question. If the majority of the 3 expert answers matches of BEM as the evaluation measure to check whether pre-
ourcollectedground-truthaccordingtoBEM[12],wecon-
|            |          |     |               |       |     |           | dictedmodelanswersmatchground-truthonesinSec. |     |     |     |     |     |     | 5.  |
| ---------- | -------- | --- | ------------- | ----- | --- | --------- | --------------------------------------------- | --- | --- | --- | --- | --- | --- | --- |
| sider that | question | to  | be answerable | given | the | Wikipedia |                                               |     |     |     |     |     |     |     |
page, andourground-truthto becorrect. Wefind thatthis C. CLIP retrieval in existing retrieval-
holdsformostofourquestions(86%).
augmentedVQAsystems[22,34]
| To put | this number |     | in context, | we also | estimate | the an- |     |     |     |     |     |     |     |     |
| ------ | ----------- | --- | ----------- | ------- | -------- | ------- | --- | --- | --- | --- | --- | --- | --- | --- |
swerabilityofA-OKVQA[46]withrespecttotheirevalua- KAT[22]andREVIVE[34]aretworetrieval-augmented
tionmetric.A-OKVQAfollowspreviousVQAdatasetsand VQA systems which use frozen CLIP [39] embeddings to
provides10ground-truthanswersperquestion. Apredicted performretrieval. Morespecifically,theyfirstencodetheir
answer is counted as correct if it matches with 3 ground- text-only knowledge base (extracted from Wikidata) using
truth answers (exact string matching [8, 46], after normal- thelanguagetowerofCLIP.Attesttime,givenaVQAques-
izationsuchasremovingpunctuationandarticles,convert- tion,theyencodeitsimageI withCLIPandthencompare
ingnumberstodigits,etc.). Sowecanconsideraquestion itsembeddingtotheknowledgebaseembeddingstoretrieve
is answerable if there are at least 3 equivalent answers out the most similar entries. The top few most similar entries
ofthe10.Wefindthat86%oftheA-OKVQAquestionsare are then passed on to a T5 model [41] which is trained to
answerable.
|     |     |     |     |     |     |     | producethefinalanswer. |     |     |     | Notehowcorrectretrievaliscru- |     |     |     |
| --- | --- | --- | --- | --- | --- | --- | ---------------------- | --- | --- | --- | ----------------------------- | --- | --- | --- |
To conclude, this user study demonstrates that our cialtoperformwellonEncyclopedic-VQA,astheretrived
Encyclopedic-VQAdatasetisofveryhighquality. knowledge base entries should contain the answer for the
|     |     |     |     |     |     |     | overall | system |     | to succeed. | Hence, | we now | test the | CLIP- |
| --- | --- | --- | --- | --- | --- | --- | ------- | ------ | --- | ----------- | ------ | ------ | -------- | ----- |
B.QualityofBEM[12]evaluationmeasure based retrieval component of KAT [22] and REVIVE [34]
inisolation,toestimatewhethertheywouldbeabletosuc-
| We want | to  | understand | how | well the | BERT | Matching |     |     |     |     |     |     |     |     |
| ------- | --- | ---------- | --- | -------- | ---- | -------- | --- | --- | --- | --- | --- | --- | --- | --- |
ceedonEncyclopedic-VQA.
(BEM)[12]evaluationmeasuremirrorshumanjudgments. WerepresentWikipediaarticlesastheCLIPembedding
| To do so | we build | on  | the user | study above | and | ask an ex- |     |       |           |             |         |               |     |         |
| -------- | -------- | --- | -------- | ----------- | --- | ---------- | --- | ----- | --------- | ----------- | ------- | ------------- | --- | ------- |
|          |          |     |          |             |     |            | of  | their | title and | description | strings | concatenated. |     | At test |
pert human to judge whether each answer from the user time, given a VQA question, we use CLIP to embed the
studymatchesthecollectedground-truthanswerornot.The
judgehasaccesstoboththequestionQandthecorrespond-
| ing Wikipedia |            | page. | We find | that the          | BEM | judgements   |     |        |     |     | Recall |     |     |     |
| ------------- | ---------- | ----- | ------- | ----------------- | --- | ------------ | --- | ------ | --- | --- | ------ | --- | --- | --- |
| equal human   | judgements |       | in      | the vast majority |     | of the cases |     |        |     |     |        |     |     |     |
|               |            |       |         |                   |     |              |     | Method |     | @1  | @5     | @10 | @20 |     |
(96%). BEMisastricterjudgein3%ofthecases,thehu-
man in 1%. This demonstrates that BEM correlates very CLIP[39] 3.3% 7.7% 12.1% 16.5%
highlywithhumanjudgementofcorrectnessofananswer, Lens[1] 47.4% 62.5% 64.7% 65.2%
confirmingwhatreportedin[12]forotherdatasets.
Exact Match. Many VQA datasets perform exact string Table7: RecallresultsforCLIPandLens.Wereporttherecall
inretrievingtherightKBarticlewithinthetop-Kdocuments.
| matchingfortheirevaluation[8,21,37,46]. |     |     |     |     |     | Becauseitis |     |     |     |     |     |     |     |     |
| --------------------------------------- | --- | --- | --- | --- | --- | ----------- | --- | --- | --- | --- | --- | --- | --- | --- |

imageItoformthequeryforretrieval,proceedingasin[22, E.DatasetStatistics
| 34]. Results | in  | Tab. 7 demonstrate |     | that recall | is  | low: the |     |          |      |         |        |          |          |        |
| ------------ | --- | ------------------ | --- | ----------- | --- | -------- | --- | -------- | ---- | ------- | ------ | -------- | -------- | ------ |
|              |     |                    |     |             |     |          |     | In Table | 8 we | provide | a more | detailed | overview | of the |
correctWikipediaarticlecorrespondingtothesubjectofthe
|          |              |        |                |      |      |        | datasetstatistics. |     |     | FormoredetailsseeSection4ofthemain |     |     |     |     |
| -------- | ------------ | ------ | -------------- | ---- | ---- | ------ | ------------------ | --- | --- | ---------------------------------- | --- | --- | --- | --- |
| question | is retrieved | in the | first position | only | 3.3% | of the |                    |     |     |                                    |     |     |     |     |
paper.
| time. Even        | within | the top-20                     | results, | retrieval | accuracy | is  |     |     |     |     |     |     |     |     |
| ----------------- | ------ | ------------------------------ | -------- | --------- | -------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| stillonlyat16.5%. |        | ThissuggestthatKAT/REVIVEwould |          |           |          |     |     |     |     |     |     |     |     |     |
F.Promptsfordatasetcreation
notworkwellonourdataset.
Lens retrieval. In Sec. 5.3 we use Google Lens to iden- AsdiscussedinSection4ofthepaper,weusedaFLAN
tify the subject C of a VQA question in our datatset, i.e. [18] version of the PaLM [17] model to (1) rephrase au-
a iNat21 or GLDv2 category, which works well (Tab. 7). tomatically generated single-hop questions (both single-
Lens is an image-based system which indexes billions of answer and multi-answer), and to (2) combine two single-
| images on | the web | and retrieves |     | relevant | ones | based on |     |     |     |     |     |     |     |     |
| --------- | ------- | ------------- | --- | -------- | ---- | -------- | --- | --- | --- | --- | --- | --- | --- | --- |
hopquestionsintoatwo-hopquestion.Generally,wefound
their visual similarity. Hence, it has a greater chance to that the model tends to work better when fed with very
findawebimageresemblingaVQAtestimage,thanwhen specific(andsometimesredundant)instructions,combined
restricting the search only to Wikipedia. Attached to the withchain-of-thoughtprompts[54]wherepossible. Addi-
image is often various meta-data which enables to deter- tionally,weaddeddifficultcasesasexamplesintheprompt,
minethenameofthesubjectcategory(whichwethenuseto
|                             |     |               |                         |         |           |       | to     | guide | the model’s | behavior |     | in more | challenging | situa- |
| --------------------------- | --- | ------------- | ----------------------- | ------- | --------- | ----- | ------ | ----- | ----------- | -------- | --- | ------- | ----------- | ------ |
| findtherightWikipediapage). |     |               | Recognizinglandmarksand |         |           |       | tions. |       |             |          |     |         |             |        |
| species from                | the | natural world | are                     | typical | use-cases | so we |        |       |             |          |     |         |             |        |
F.1.Rephrasingautomaticallygeneratedsingle-hop
| can expect | Lens | to work well | for | recognizing | the | subjects |     |     |     |     |     |     |     |     |
| ---------- | ---- | ------------ | --- | ----------- | --- | -------- | --- | --- | --- | --- | --- | --- | --- | --- |
questions
| C inourdataset. |     | However,academicresultsoniNat21[4] |     |     |     |     |     |     |     |     |     |     |     |     |
| --------------- | --- | ---------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
andGLDv2[2]suggeststhatspecializedsystemsworkvery
|     |     |     |     |     |     |     |     | We replaced |     | the name | of the | category | C   | in the ques- |
| --- | --- | --- | --- | --- | --- | --- | --- | ----------- | --- | -------- | ------ | -------- | --- | ------------ |
wellonthesedatasets,suggestingthatagoodimage-based tionbyitssupercategory. Thisrephrasingprocesswasrun
classifierwouldmakeaviablesubstituteforLens.
forallautomaticallygeneratedsingle-hopquestions(single
Generally, the core points of our paper are that (1) En- andmulti-answer). Forexample,thequestionWhereisthe
| cyclopedic-VQA |     | poses a | hard challenge |     | for large | LLMs | E´glise |              |     |          |     |               |     |             |
| -------------- | --- | ------- | -------------- | --- | --------- | ---- | ------- | ------------ | --- | -------- | --- | ------------- | --- | ----------- |
|                |     |         |                |     |           |      |         | Saint-Cannat |     | located? |     | was rephrased |     | to Where is |
and VLMs, and (2) solving it requires augmenting the thischurchlocated? Afterseveraliterations,wedecidedto
LLM/VLM with a retrieval component to access a knowl- add three examples in the prompt, using chain-of-thought
| edgebase. | Theexactchoiceofretrievalcomponentisflex- |     |     |     |     |     |           |     |      |              |     |       |           |        |
| --------- | ----------------------------------------- | --- | --- | --- | --- | --- | --------- | --- | ---- | ------------ | --- | ----- | --------- | ------ |
|           |                                           |     |     |     |     |     | reasoning |     | with | three steps. | The | model | generally | writes |
ibleandlikelysubjecttofurtherexploration. the three required steps in the output, and we parse the
|     |     |     |     |     |     |     | rephrased |     | question | right | after the | third | step written | by the |
| --- | --- | --- | --- | --- | --- | --- | --------- | --- | -------- | ----- | --------- | ----- | ------------ | ------ |
D.Qualitativeresults model. Sometimes, the model would return an incorrectly
|     |     |     |     |     |     |     | rephrased |     | question. | We observe |     | that the | main | failure case |
| --- | --- | --- | --- | --- | --- | --- | --------- | --- | --------- | ---------- | --- | -------- | ---- | ------------ |
We present qualitative results for some of the methods is when the model simply copies the input question to the
we experimented with in Figures 6 and 7. These illustrate output. Thus, we filter out any rephrased question that is
caseswherethedifferentsetupsmaywork,andcaseswhere
|     |     |     |     |     |     |     | identical |     | to the | original question |     | fed in | its input. | We sam- |
| --- | --- | --- | --- | --- | --- | --- | --------- | --- | ------ | ----------------- | --- | ------ | ---------- | ------- |
allofthemfail. Forexample,givenatextualquestionWhat pled100rephrasedquestionstomanuallyassessthequality
does this reptile eat?, PaLM can reasonably guess an an- of the rephrasings, which we found to be correct in 90%
swer without any additional context: insects – since that’s ofthecases. ThefinalpromptisgiveninPrompt1,where
whatmanyreptileseat. “$C$” and “$Q$” are placeholders for the category C and
thetextual(pre-rephrasing)questionQ.
Formorespecificinformation,relatingtomoredetailed
properties(e.g.,numberofeggsaspecificreptilelays,dates,
|     |     |     |     |     |     |     | F.2. | Chaining |     | single-hop | questions |     | into | two-hop |
| --- | --- | --- | --- | --- | --- | --- | ---- | -------- | --- | ---------- | --------- | --- | ---- | ------- |
howbigaspecificfishmaybecome),largemodelsmaystill
questions
| makereasonableguesses, |                                      |     | butgenerallythisleadstoincor- |     |     |     |     |            |     |                   |     |             |     |             |
| ---------------------- | ------------------------------------ | --- | ----------------------------- | --- | --- | --- | --- | ---------- | --- | ----------------- | --- | ----------- | --- | ----------- |
| rectanswers.           | Augmentinglargemodelswithcontextfrom |     |                               |     |     |     |     |            |     |                   |     |             |     |             |
|                        |                                      |     |                               |     |     |     |     | We created |     | two-hop questions |     | by chaining |     | two single- |
retrieved knowledge improves the accuracy in these cases hopquestions,wheretheanswertothefirstsingle-hopques-
substantially,leadingtopreciseanswersthatareattributable tionservesasabridgeentitythatmakesaconnectiontothe
tothepieceofknowledgethatwasretrieved.
|     |     |     |     |     |     |     | second | single-hop |     | question. | For | example, | given | the first |
| --- | --- | --- | --- | --- | --- | --- | ------ | ---------- | --- | --------- | --- | -------- | ----- | --------- |
Finally, we also see cases where the generated answer single-hopquestion(SQ1)Whatisthemaincompetitorfor
is incorrect, even if the correct piece of knowledge is re- food for this animal? with answer (SA1) Spotted hyena,
trieved. Thisindicatesthatinsomecaseslargemodelsmay andthesecondsingle-hopquestion(SQ2)Whatisthepop-
stillhavedifficultiesusingretrievedknowledgetogenerate ulation size of this animal? with answer (SA2) Between
accurateanswers. 27,000 and 47,000 individuals, we generated What is the

Qualitative results: PaLI
|     | In which direction from the  | What is Mary handling  |     |     |     |     |     |     |
| --- | ---------------------------- | ---------------------- | --- | --- | --- | --- | --- | --- |
In what month of 1944 was this What was this church fitted with
| Question | german-czech border is this  | St.Dominic in the facade of this  |           |     |                   |     |          |     |
| -------- | ---------------------------- | --------------------------------- | --------- | --- | ----------------- | --- | -------- | --- |
|          |                              |                                   |           |     | church destroyed? |     | in 1914? |     |
|          | mountain located?            |                                   | building? |     |                   |     |          |     |
No
|     | north |     | child |     | december |     | window |     |
| --- | ----- | --- | ----- | --- | -------- | --- | ------ | --- |
Retrieval
Lens
|     | north |     | rosary |     | december |     | bell |     |
| --- | ----- | --- | ------ | --- | -------- | --- | ---- | --- |
Section
Oracle
|     | north |     | rosary |     | may |     | steeple |     |
| --- | ----- | --- | ------ | --- | --- | --- | ------- | --- |
Section
Ground
|     | north |     | the rosary |     | may |     | electricity |     |
| --- | ----- | --- | ---------- | --- | --- | --- | ----------- | --- |
Truth
Figure 6:
PaLIqualitativeresults. Wepresent4multi-modalquestionsandtheanswersproducedwithdifferentexperimentalsetups,
usingthePaLImodel: withoutretrieval(“NoRetrieval”), Lens-basedretrievalofKBsection(“LensSection”), OracleretrievalofKB
secQtionu(“aOrlaictleaSetcitvione”) .rAeddsitiounalltlys,w:e Fprolvaidenth-ePgroaunLd-tMruthanswerinthelastrow.Weshowcaseswhereeachofthesetupscan
producethecorrectanswer,aswellasanexamplewhereallmethodsfail(theright-mostone).
|     |     | How many eggs does this  |     |     |     | How big does this fish typically  |     |     |
| --- | --- | ------------------------ | --- | --- | --- | --------------------------------- | --- | --- |
Question What does this reptile eat? What does this animal eat?
|     |     | reptile typically lay? |     |     |     |     | become? |     |
| --- | --- | ---------------------- | --- | --- | --- | --- | ------- | --- |
No
|     | insects |     | 10-15 |     | insects |     | 6 cm |     |
| --- | ------- | --- | ----- | --- | ------- | --- | ---- | --- |
Retrieval
Lens
|     | insects |     | 3-6 |     | plankton |     | 13 cm |     |
| --- | ------- | --- | --- | --- | -------- | --- | ----- | --- |
Section
| Oracle  | insects |     | 3-6 |     | mussels |     | 13 cm |     |
| ------- | ------- | --- | --- | --- | ------- | --- | ----- | --- |
Section
Ground
|     | insects |     | 3-6 |     | mussels |     | 25 cm |     |
| --- | ------- | --- | --- | --- | ------- | --- | ----- | --- |
Truth
Figure7: PaLMqualitativeresults. Wepresent4multi-modalquestionsandtheanswersproducedwithdifferentexperimentalsetups,
usingthePaLMmodel: withoutretrieval(“NoRetrieval”),Lens-basedretrievalofKBsection(“LensSection”),OracleretrievalofKB
section(“OracleSection”).Additionally,weprovidetheground-truthanswerinthelastrow.Weshowcaseswhereeachofthesetupscan
producethecorrectanswer,aswellasanexamplewhereallmethodsfail(theright-mostone).
| population | size of the main competitor | for food | of this an- | individuals. |     |     |     |     |
| ---------- | --------------------------- | -------- | ----------- | ------------ | --- | --- | --- | --- |
imal? Inthisexample,theentityspottedhyenaservesasa
|                                         |     |     |             | After | several iterations, | we crafted | a prompt | with 4 ex- |
| --------------------------------------- | --- | --- | ----------- | ----- | ------------------- | ---------- | -------- | ---------- |
| bridgebetweenthetwosingle-hopquestions. |     |     | Notethatthe |       |                     |            |          |            |
answertothetwo-hopquestionisidenticaltotheanswerto amplesofvaryingdifficulty(Prompt2). Theoutputofthe
|     |     |     |     | model | is taken as the chained | two-hop | question. | Some- |
| --- | --- | --- | --- | ----- | ----------------------- | ------- | --------- | ----- |
thesecondsingle-hopquestion,Between27,000and47,000
times,though,themodelwouldreturnanincorrecttwo-hop

NumberofQ+Apairs NumberofCategories %UniqueC Total(I,Q,A)triplets
|     |     |     | Train | Val | Test | Train | Val | Test | Val | Test | Train | Val | Test |     |
| --- | --- | --- | ----- | --- | ---- | ----- | --- | ---- | --- | ---- | ----- | --- | ---- | --- |
Templated 8,133 200 500 1,983 114 300 26% 21% 40,665 1,000 500
12taNi Automatic 96,317 1,000 1,500 4,061 343 562 5% 6% 481,585 5,000 1,500
MultiAnswer 11,262 200 500 3,585 118 318 31% 22% 56,310 1,000 500
|     |     | TwoHop    | 10,819 | 200 | 500 | 2,499 | 131 | 359 | 11% | 6%  | 54,095 | 1,000 | 500 |     |
| --- | --- | --------- | ------ | --- | --- | ----- | --- | --- | --- | --- | ------ | ----- | --- | --- |
|     |     | Templated | 5,795  | 200 | 500 | 1,808 | 135 | 268 | 27% | 28% | 25,870 | 827   | 500 |     |
2vDLG
Automatic 57,124 750 1,250 1,965 138 208 9% 9% 255,529 3,025 1,250
MultiAnswer 12,667 200 500 6,449 151 339 45% 56% 56,426 844 500
|     |     | TwoHop | 10,221 | 200 | 500 | 1,405 | 99  | 154 | 11% | 11% | 45,771 | 895 | 500 |     |
| --- | --- | ------ | ------ | --- | --- | ----- | --- | --- | --- | --- | ------ | --- | --- | --- |
Total 212,338 2,950 5,750 16,249 1,071 2,152 14% 17% 1,016,251 13,591 5,750
Table8: DatasetStatistics. Wereportthenumberofquestion,answer,categoriesandtripletsforbothsupportingdatasetsanddifferent
questiontypes.Intotalourdatasetcontains1MVQAtriplets,makingitthelargestofitskind.
question. Forthisreason, wedesignedasecondpromptto evantsectionswithPaLM.Whennotavailable,werevertto
| validatethetwo-hopquestionprovidedastheinitialoutput |     |     |     |     |     |     |     | usingPrompt4. |     |     |     |     |     |     |
| ---------------------------------------------------- | --- | --- | --- | --- | --- | --- | --- | ------------- | --- | --- | --- | --- | --- | --- |
(Prompt3).Themodelisaskedtoanswerthetwo-hopques-
G.2.Promptforrelevantsectionidentification
tionusingthetwoinitialsingle-hopquestionswithanswers
ascontext.Ifthepredictedanswerisidenticaltotheanswer
|     |     |     |     |     |     |     |     | We use | Prompt | 8 to | identify | relevant | sections | in a |
| --- | --- | --- | --- | --- | --- | --- | --- | ------ | ------ | ---- | -------- | -------- | -------- | ---- |
of the second single-hop question, then the two-hop ques- Wikipedia article retrieved by Lens. To do so, we query
tionisvalidatedandkeptinourdataset;otherwise,itisdis-
PaLMforeachsection$S$intheLensretrievedWikipedia
carded. Finally,wefiltersomecommonfailurecases,such article for a question $Q$. Note that we use a few exam-
aswhenthemodeloutputstheexactfirstsingle-hoporsec- plesintheprompttoconditionPaLMtogenerateayes/no
| ond single-hop |     | question | as the | chained | two-hop | question. |     |         |                                         |     |     |     |     |     |
| -------------- | --- | -------- | ------ | ------- | ------- | --------- | --- | ------- | --------------------------------------- | --- | --- | --- | --- | --- |
|                |     |          |        |         |         |           |     | answer. | TheanswerproducedbyPaLMisconvertedintoa |     |     |     |     |     |
We sampled 100 chained two-hop questions to manually stringandmatchedtoeitheryesorno. Whennomatchis
| assess their | quality, | which | we  | found | to be correct | in 88% |     |           |        |              |        |          |     |           |
| ------------ | -------- | ----- | --- | ----- | ------------- | ------ | --- | --------- | ------ | ------------ | ------ | -------- | --- | --------- |
|              |          |       |     |       |               |        |     | found, we | assume | that section | is not | relevant | to  | the ques- |
ofthecases. Inthefinalprompts(Prompts2-3)“$SQ1$”/ tion. Ifmorethanonesectionisidentifiedasrelevantfora
“$SA1$”areplaceholdersforthefirstsingle-hopquestion/ question, the input $S$ to Prompt 7 becomes the concate-
answer,“$SQ2$”/“$SA2$”areplaceholdersforthesecond
nationofallrelevantsections.
single-hopquestion/answer,and“$Q$”istheplaceholder
forthegeneratedtwo-hopquestion.
G.Promptsforevaluation
InSection5weanalyzetheperformanceoflargemodels
| inourdataset. | Similarlytothedatasetcreationprocess,we |     |                           |             |     |          |     |     |     |     |     |     |     |     |
| ------------- | --------------------------------------- | --- | ------------------------- | ----------- | --- | -------- | --- | --- | --- | --- | --- | --- | --- | --- |
| use diverse   | prompts                                 | to  | adapt                     | and improve | the | behavior | of  |     |     |     |     |     |     |     |
| thesemodels.  | Inparticular,                           |     | weusepromptstoincorporate |             |     |          |     |     |     |     |     |     |     |     |
retrievalresultsandtoidentifyrelevantsectionsforarticles
retrievedwithLens.
G.1.Promptsforquestionanswering
| We use         | textual  | Prompts       | 4-7      | to produce   |              | answers with  |     |     |     |     |     |     |     |     |
| -------------- | -------- | ------------- | -------- | ------------ | ------------ | ------------- | --- | --- | --- | --- | --- | --- | --- | --- |
| large models.  | Note     | that          | we       | use the      | same prompts | when          |     |     |     |     |     |     |     |     |
| using PaLI     | or PaLM. |               | However, | we always    |              | use the ques- |     |     |     |     |     |     |     |     |
| tion image     | I as     | an additional |          | input to     | the model    | when us-      |     |     |     |     |     |     |     |     |
| ing PaLI.      | Note     | that we       | use $Q$  | to refer     | to the       | textual part  |     |     |     |     |     |     |     |     |
| of a question, | $C$      | to denote     |          | the question | subject      | C name,       |     |     |     |     |     |     |     |     |
$Art$todenotethefulltextofaWikipediaarticleand$S$
| todenoteasectionofaWikipediaarticle.    |     |     |              |       | Weusethesame |               |     |     |     |     |     |     |     |     |
| --------------------------------------- | --- | --- | ------------ | ----- | ------------ | ------------- | --- | --- | --- | --- | --- | --- | --- | --- |
| retrievalpromptsforLensandOraclesetups. |     |     |              |       |              | However,for   |     |     |     |     |     |     |     |     |
| Lens experiments,                       |     | we  | only include | $Art$ | or           | $S$ if avail- |     |     |     |     |     |     |     |     |
able,aswemightnotretrieveentitieswithLensorfindrel-

| Prompt1: | Rephrasingautomatically-generatedsinglehopquestion |     |     |     |     |     |     |
| -------- | -------------------------------------------------- | --- | --- | --- | --- | --- | --- |
In this task, please rephrase the question by replacing the entity name by the word "this",
| followed | by the type | of the | entity. | See the | examples | below: |     |
| -------- | ----------- | ------ | ------- | ------- | -------- | ------ | --- |
EXAMPLE 1:
| entity    | name: eiffel  | tower    |        |              |     |        |           |
| --------- | ------------- | -------- | ------ | ------------ | --- | ------ | --------- |
| question: | How tall      | is the   | eiffel | tower?       |     |        |           |
| step 1    | (find type of | entity): | The    | eiffel tower | is  | a type | of: tower |
step 2 (write the word "this" followed by the type obtained in "step 1"): this tower
| step 3 | (final rephrased | question): |     | How tall | is this | tower? |     |
| ------ | ---------------- | ---------- | --- | -------- | ------- | ------ | --- |
EXAMPLE 2:
| entity    | name: salmon  |          |             |           |        |         |      |
| --------- | ------------- | -------- | ----------- | --------- | ------ | ------- | ---- |
| question: | Which country | is       | the largest | producer  | of     | salmon? |      |
| step 1    | (find type of | entity): | The         | salmon is | a type | of:     | fish |
step 2 (write the word "this" followed by the type obtained in "step 1"): this fish
step 3 (final rephrased question): Which country is the largest producer of this fish?
EXAMPLE 3:
| entity | name: Grand | Beach Provincial |     | Park |     |     |     |
| ------ | ----------- | ---------------- | --- | ---- | --- | --- | --- |
question: grand beach provincial park is located on the east side of what lake?
step 1 (find type of entity): The Grand Beach Provincial Park is a type of: park
step 2 (write the word "this" followed by the type obtained in "step 1"): this park
step 3 (final rephrased question): this park is located on the east side of what lake?
Note that the entity name should not be part of the rephrased question.
| Please | make sure to | write out | all | 3 steps as | in the | examples | above. |
| ------ | ------------ | --------- | --- | ---------- | ------ | -------- | ------ |
Based on the above examples, provide a rephrased question for the following case:
| entity    | name: $C$ |     |     |     |     |     |     |
| --------- | --------- | --- | --- | --- | --- | --- | --- |
| question: | $Q$       |     |     |     |     |     |     |

| Prompt2: | Chainingtwosingle-hopquestionsintoatwo-hopquestion |     |     |     |     |     |     |
| -------- | -------------------------------------------------- | --- | --- | --- | --- | --- | --- |
EXAMPLE 1:
| question | 1: in which      | city   | is this | building    | located? |            |     |
| -------- | ---------------- | ------ | ------- | ----------- | -------- | ---------- | --- |
| answer   | 1: San Francisco |        |         |             |          |            |     |
| question | 2: what          | is the | average | temperature | in San   | Francisco? |     |
| answer   | 2: 15 Celsius    |        |         |             |          |            |     |
combined question: What is the average temperature in the city where this building is located?
EXAMPLE 2:
| question | 1: what          | is the | predator  | of this | animal?     |     |     |
| -------- | ---------------- | ------ | --------- | ------- | ----------- | --- | --- |
| answer   | 1: Lion          |        |           |         |             |     |     |
| question | 2: What          | is the | weight of | a lion  | on average? |     |     |
| answer   | 2: 190 kilograms |        |           |         |             |     |     |
combined question: What is the average weight of the predator of this animal?
EXAMPLE 3:
| question | 1: In what    | country          | is this  | plant | found?    |              |     |
| -------- | ------------- | ---------------- | -------- | ----- | --------- | ------------ | --- |
| answer   | 1: Australia  |                  |          |       |           |              |     |
| question | 2: What       | does australia’s |          | size  | give it a | wide variety | of? |
| answer   | 2: landscapes | and              | climates |       |           |              |     |
combined question: What does the size of the country where this plant is found give it a wide
variety of?
EXAMPLE 4:
| question | 1: What         | country | is this | plant | the national | flower | of? |
| -------- | --------------- | ------- | ------- | ----- | ------------ | ------ | --- |
| answer   | 1: South Africa |         |         |       |              |        |     |
question 2: south africa is a member of the commonwealth of nations and what other
organization?
| answer | 2: the G20 |     |     |     |     |     |     |
| ------ | ---------- | --- | --- | --- | --- | --- | --- |
combined question: The country that this plant is the national flower of is a member of the
| commonwealth | of nations | and | what | other | organization? |     |     |
| ------------ | ---------- | --- | ---- | ----- | ------------- | --- | --- |
Based on the above 4 examples, provide a combined question for the following case, such that
the answer to the combined question is the same as the answer to question 2:
| question | 1: $SQ1$                                                     |     |     |     |     |     |     |
| -------- | ------------------------------------------------------------ | --- | --- | --- | --- | --- | --- |
| answer   | 1: $SA1$                                                     |     |     |     |     |     |     |
| question | 2: $SQ2$                                                     |     |     |     |     |     |     |
| answer   | 2: $SA2$                                                     |     |     |     |     |     |     |
| combined | question:                                                    |     |     |     |     |     |     |
| Prompt3: | Validatingtwo-hopquestiongiventheoriginalsingle-hopquestions |     |     |     |     |     |     |
| question | 1: $SQ1$                                                     |     |     |     |     |     |     |
| answer   | 1: $SA1$                                                     |     |     |     |     |     |     |
| question | 2: $SQ2$                                                     |     |     |     |     |     |     |
| answer   | 2: $SA2$                                                     |     |     |     |     |     |     |
Based on the questions and answers above, please answer the following question: $Q$

| Prompt4:   | Questiononlyevaluation      |        |                |          |              |     |
| ---------- | --------------------------- | ------ | -------------- | -------- | ------------ | --- |
| Question:  | $Q$                         |        |                |          |              |     |
| The answer | is:                         |        |                |          |              |     |
| Prompt5:   | Retrievalwithentityname     |        |                |          |              |     |
| Entity     | name:                       | $C$    |                |          |              |     |
| Question:  | $Q$                         |        |                |          |              |     |
| The answer | is:                         |        |                |          |              |     |
| Prompt6:   | RetrievalwithKBArticle      |        |                |          |              |     |
| Context:   | $Art$                       |        |                |          |              |     |
| Question:  | $Q$                         |        |                |          |              |     |
| The answer | is:                         |        |                |          |              |     |
| Prompt7:   | RetrievalwithKBSection      |        |                |          |              |     |
| Context:   | $S$                         |        |                |          |              |     |
| Question:  | $Q$                         |        |                |          |              |     |
| The answer | is:                         |        |                |          |              |     |
| Prompt8:   | LensretrievaltogetKBSection |        |                |          |              |     |
| Can the    | answer                      | to the | question       | be found | in the text? |     |
| Question:  | Is                          | this   | fungus edible? |          |              |     |
Text: This compound induces mammalian cells (specifically, the cell line HL60 to differentiate
into granulocyte- or macrophage-like cells. The fungus also contains the mycotoxin muscarine,
and the antifungal metabolite strobilurin D. Despite the presence of these toxins, some guides
| list this  | fungus | safe   | for human | consumption.    |               |     |
| ---------- | ------ | ------ | --------- | --------------- | ------------- | --- |
| The answer | is:    | yes    |           |                 |               |     |
| Can the    | answer | to the | question  | be found        | in the text?  |     |
| Question:  | In     | which  | season    | does this plant | give flowers? |     |
Text: This cactus has stems about 1/2-1 inch wide with 6-9 edges. Its flowers are white, up
to 30 centimetres in diameter with a scent redolent of vanilla. The flowers open after sundown,
closing and wasting after a few hours. By 9 am the next day they are gone.
| The answer | is:    | no     |             |          |              |     |
| ---------- | ------ | ------ | ----------- | -------- | ------------ | --- |
| Can the    | answer | to the | question    | be found | in the text? |     |
| Question:  | What   | is     | the habitat | of this  | animal?      |     |
Text: X is native to Europe and North Africa through to Central Asia. It is introduced to the
United States and parts of South America. It widespread across the northeastern United States
and eastern Canada, and can be found outside, or more commonly inside houses. It is thought to
| have been  | introduced |        | into America | from     | Europe by English | colonists. |
| ---------- | ---------- | ------ | ------------ | -------- | ----------------- | ---------- |
| The answer | is:        | yes    |              |          |                   |            |
| Can the    | answer     | to the | question     | be found | in the text?      |            |
| Question:  | $Q$        |        |              |          |                   |            |
Text: $S$
| The answer | is: |     |     |     |     |     |
| ---------- | --- | --- | --- | --- | --- | --- |