q116/random_k4/random_3.md

|     |     | Learning |            | Fine-Grained |       | Grounded | Citations |     | for |     |     |
| --- | --- | -------- | ---------- | ------------ | ----- | -------- | --------- | --- | --- | --- | --- |
|     |     |          | Attributed |              | Large | Language | Models    |     |     |     |     |
LeiHuang1,XiaochengFeng1,2*,WeitaoMa1,YuxuanGu1,WeihongZhong1,XiachongFeng1
WeijiangYu3,WeihuaPeng3,DuyuTang3,DandanTu3,BingQin1,2
1HarbinInstituteofTechnology,Harbin,China
|     |     | 2 PengChengLaboratory |     |     |     | 3HuaweiInc.,Shenzhen,China |     |     |     |     |     |
| --- | --- | --------------------- | --- | --- | --- | -------------------------- | --- | --- | --- | --- | --- |
{lhuang,xcfeng,wtma,yxgu,whzhong,xiachongfeng,qinb}@ir.hit.edu.cn
{weijiangyu8,pengwh.hit}@gmail.com,{tangduyu,tudandan}@huawei.com
Abstract
Why is it bad to eat cookie dough for risk of salmonella but things
like Cookie Dough Bites are ok?
| Despite | the | impressive | performance |     | on  |                                                          |     |     |     |     |     |
| ------- | --- | ---------- | ----------- | --- | --- | -------------------------------------------------------- | --- | --- | --- | --- | --- |
|         |     |            |             |     |     | Salmonella is most commonly caused by eating undercooked |     |     |     |     | 1   |
information-seekingtasks,largelanguagemod- or raw foods like eggs or meat. (...) You know how your mom
4202 guA 8  ]LC.sc[  1v86540.8042:viXra els (LLMs) still struggle with hallucinations. always warned you not to eat raw cookie dough? This is why.
| Attributed | LLMs, | which | augment | generated |     |                                                                 |     |     |     |     |     |
| ---------- | ----- | ----- | ------- | --------- | --- | --------------------------------------------------------------- | --- | --- | --- | --- | --- |
|            |       |       |         |           |     | Food safety recommendations encourage people to cook eggs until |     |     |     |     | 2   |
text within-line citations, haveshownpoten- the white and yolk are firm in order to kill any bacteria. (...) However,
tial in mitigating hallucinations and improv- anyone making cookies can do things to reduce this risk by
using pasteurized egg products.
| ingverifiability. |     | However,currentapproaches |     |     |     |     |     |     |     |     |     |
| ----------------- | --- | ------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
suffer from suboptimal citation quality due Raw cookie dough can carry the risk of salmonella contamination, which can
to their reliance on in-context learning. Fur- lead to foodborne illness if the dough is eaten raw  1  . However, products
like Cookie Dough Bites are generally safe because they are made with
| thermore, | the | practice | of citing | only | coarse | pasteurized eggs  1    2  . |     |     |     |     |     |
| --------- | --- | -------- | --------- | ---- | ------ | --------------------------- | --- | --- | --- | --- | --- |
documentidentifiersmakesitchallengingfor
Difficult to verify and subject to
userstoperformfine-grainedverification. In hallucinations and citation errors
| this work, | we  | introduce | FRONT, | a   | training |     |     |     |     |     |     |
| ---------- | --- | --------- | ------ | --- | -------- | --- | --- | --- | --- | --- | --- |
 G  R  O   U  N  D  I N  G     1  salmonella is most commonly caused by eating
framework designed to teach LLMs to gen- undercooked or raw foods like eggs or meat  2  Anyone making cookies
can do things to reduce this risk by using pasteurized egg products.
erateFine-gRainedgrOuNdedciTations. By  A  N  S  W   E  R    Eating raw cookie dough can be risky due to salmonella,
groundingmodeloutputsinfine-grainedsup- commonly found in raw eggs  1 . However, this concern is mitigated in
products like Cookie Dough Bites, as the products use pasteurized egg
portingquotes,thesequotesguidethegenera-
products, which are safe to eat raw 2  .
tionofgroundedandconsistentresponses,not
onlyimprovingcitationqualitybutalsofacili- Interpretable and accurately cited
tatingfine-grainedverification.Experimentson
|     |     |     |     |     |     | Figure1: | Comparedwiththecurrentattributedsystems, |     |     |     |     |
| --- | --- | --- | --- | --- | --- | -------- | ---------------------------------------- | --- | --- | --- | --- |
theALCEbenchmarkdemonstratetheefficacy
thecoreideabehindFRONTistofirstselectthesupport-
ofFRONTingeneratingsuperiorgroundedre-
|                                      |     |     |     |     |      | ing quotes | from | retrieved | sources and | then | condition |
| ------------------------------------ | --- | --- | --- | --- | ---- | ---------- | ---- | --------- | ----------- | ---- | --------- |
| sponsesandhighlysupportivecitations. |     |     |     |     | With |            |      |           |             |      |           |
thegenerationprocessonthem,ensuringgroundedre-
LLaMA-2-7B,theframeworksignificantlyout-
sponsesandaccuratecitations.
| performsallthebaselines, |     |     | achievinganaver- |     |     |     |     |     |     |     |     |
| ------------------------ | --- | --- | ---------------- | --- | --- | --- | --- | --- | --- | --- | --- |
ageof14.21%improvementincitationquality
acrossalldatasets,evensurpassingChatGPT1. SuchprevalenceofhallucinationsinLLMout-
|     |     |     |     |     |     | puts has | motivated | the | development | of  | attributed |
| --- | --- | --- | --- | --- | --- | -------- | --------- | --- | ----------- | --- | ---------- |
1 Introduction
|     |     |     |     |     |     | systems | (Nakano | et al., | 2021; Thoppilan |     | et al., |
| --- | --- | --- | --- | --- | --- | ------- | ------- | ------- | --------------- | --- | ------- |
Therecentadventoflargelanguagemodels(LLMs) 2022;Menicketal.,2022),suchasNewBing2 and
(Touvronetal.,2023;OpenAI,2023;Zhaoetal., Perplexity3,whereLLMsareallowedtogenerate
2023) has taken the world by storm, fueling a responses with in-line citations. Not only does it
| paradigm | shift in | information |     | acquisition | (Zhu |     |     |     |     |     |     |
| -------- | -------- | ----------- | --- | ----------- | ---- | --- | --- | --- | --- | --- | --- |
improvefactualityandalleviatehallucinations,but
etal.,2023). Despitetheircompellingperformance, italsosimplifiesuserverificationofmodeloutputs,
LLMs still struggle with hallucinations (Ji et al., furtherenhancingtheverifiabilityofLLMs.
2023;Huangetal.,2023),atendencytofabricate Despiterecentadvancements,currentattributed
| non-existent | facts | or generate | unfaithful |     | content. |                                        |     |     |     |     |          |
| ------------ | ----- | ----------- | ---------- | --- | -------- | -------------------------------------- | --- | --- | --- | --- | -------- |
|              |       |             |            |     |          | LLMsstillexposesignificantlimitations. |     |     |     |     | Firstly, |
This issue further poses a risk of misinformation current approaches predominantly rely on either
dissemination(ChenandShu,2023),directlyim- in-contextlearning(Gaoetal.,2023b)orpost-hoc
pactingthereliabilityandtrustworthinessofLLMs.
retrieval(Gaoetal.,2023a)toachieveattribution,
*CorrespondingAuthor
1Our data and code can be found at: https://github. 2https://www.bing.com/chat
com/LuckyyySTA/Fine-grained-Attribution. 3https://www.perplexity.ai

|         |             |             |     |            |     |        | 2 RelatedWork |     |     |     |     |     |     |
| ------- | ----------- | ----------- | --- | ---------- | --- | ------ | ------------- | --- | --- | --- | --- | --- | --- |
| lacking | an inherent | attribution |     | capability |     | within |               |     |     |     |     |     |     |
LLMs,therebyresultingincompromisedcitation
|                         |           |     |                         |      |           |      | RetrievalAugmentedGeneration. |     |            |     |       | Recently,re- |     |
| ----------------------- | --------- | --- | ----------------------- | ---- | --------- | ---- | ----------------------------- | --- | ---------- | --- | ----- | ------------ | --- |
| quality(Liuetal.,2023). |           |     | Secondly,thesecitations |      |           |      |                               |     |            |     |       |              |     |
|                         |           |     |                         |      |           |      | trieval augmented             |     | generation |     | (RAG) | (Karpukhin   |     |
| are typically           | presented |     | in the                  | form | of either | doc- |                               |     |            |     |       |              |     |
ument identifiers (Nakano et al., 2021) or URLs etal.,2020;Lewisetal.,2020;Fengetal.,2023;
Gaoetal.,2023c)hasshownpromiseinknowledge-
| (Thoppilan | et al., | 2022). | Such | coarse | attribution |     |           |        |                  |     |           |     |       |
| ---------- | ------- | ------ | ---- | ------ | ----------- | --- | --------- | ------ | ---------------- | --- | --------- | --- | ----- |
|            |         |        |      |        |             |     | intensive | tasks. | By incorporating |     | retrieved |     | docu- |
complicatestheprocessforuserstoperformfine-
|     |     |     |     |     |     |     | ments, | LLMsareequippedwithup-to-dateinfor- |     |     |     |     |     |
| --- | --- | --- | --- | --- | --- | --- | ------ | ----------------------------------- | --- | --- | --- | --- | --- |
grainedverification(e.g.,pinpointspecificsupport-
|     |     |     |     |     |     |     | mation, | significantly | mitigating |     | knowledge |     | gaps. |
| --- | --- | --- | --- | --- | --- | --- | ------- | ------------- | ---------- | --- | --------- | --- | ----- |
ingevidence),particularlyinlengthydocuments.
|         |      |        |            |     |            |      | However, | recent | studies | (Shi | et al., | 2023; | Yoran |
| ------- | ---- | ------ | ---------- | --- | ---------- | ---- | -------- | ------ | ------- | ---- | ------- | ----- | ----- |
| To this | end, | we aim | to advance |     | attributed | text |          |        |         |      |         |       |       |
etal.,2023;Xuetal.,2023a;Zhuetal.,2024)have
generationbyempoweringLLMswithfine-grained
attributionability. However,onechallengecomes revealed that existing retrieval-augmented LLMs
|     |     |     |     |     |     |     | struggle | to handle | irrelevant |     | or contradictory |     | re- |
| --- | --- | --- | --- | --- | --- | --- | -------- | --------- | ---------- | --- | ---------------- | --- | --- |
fromtheacquisitionofhigh-qualityattributiondata
trievaldocumentsandeffectivelyutilizecontextual
| for supervised                       |     | fine-tuning, |     | which | is difficult | and     |           |       |             |     |            |     |         |
| ------------------------------------ | --- | ------------ | --- | ----- | ------------ | ------- | --------- | ----- | ----------- | --- | ---------- | --- | ------- |
|                                      |     |              |     |       |              |         | evidence. | These | limitations |     | can result | in  | perfor- |
| costlytoannotate,andthereforescarce. |     |              |     |       |              | Thus,we |           |       |             |     |            |     |         |
mancedegradationorevenhallucinations(Huang
| start with | an automatic |     | data | generation |     | pipeline |                |     |              |     |           |     |      |
| ---------- | ------------ | --- | ---- | ---------- | --- | -------- | -------------- | --- | ------------ | --- | --------- | --- | ---- |
|            |              |     |      |            |     |          | et al., 2023), |     | highlighting | the | necessity | for | more |
tailoredforcollectinghigh-qualityattributiondata
factualandverifiablesystems.
(§3.1). Givenauserquery,thepipelineautomates
dataconstructionthroughdocumentretrieval,rele-
|     |     |     |     |     |     |     | AttributedLargeLanguageModels. |     |     |     |     | Theper- |     |
| --- | --- | --- | --- | --- | --- | --- | ------------------------------ | --- | --- | --- | --- | ------- | --- |
vancereranking,attributedanswergeneration,and
|                                 |     |        |      |                     |     |     | sistent     | challenge | of hallucinations |     |               | within | LLMs |
| ------------------------------- | --- | ------ | ---- | ------------------- | --- | --- | ----------- | --------- | ----------------- | --- | ------------- | ------ | ---- |
| data filtering                  | to  | ensure | both | the informativeness |     |     |             |           |                   |     |               |        |      |
|                                 |     |        |      |                     |     |     | has spurred | the       | development       |     | of attributed |        | LLMs |
| andattributabilityoftheanswers. |     |        |      | Furthermore,to      |     |     |             |           |                   |     |               |        |      |
(Bohnetetal.,2022;Lietal.,2023;Worledgeetal.,
betterunlockLLMs’abilityforfine-grainedattri-
2023),whichseektoenhanceinformationverifia-
| bution,       | we introduce |      | FRONT,  | a two-stage |     | train-   |           |            |           |        |           |             |          |
| ------------- | ------------ | ---- | ------- | ----------- | --- | -------- | --------- | ---------- | --------- | ------ | --------- | ----------- | -------- |
|               |              |      |         |             |     |          | bility by | generating | responses |        | with      | attribution | to       |
| ing framework |              | that | teaches | LLMs        | to  | generate |           |            |           |        |           |             |          |
|               |              |      |         |             |     |          | evidence  | sources.   | The       | way of | providing |             | attribu- |
Fine-gRainedgrOuNdedciTations(§3.2),consist-
|        |           |        |            |     |      |     | tionsvariesacrossstudies. |     |     | Forexample,Gaoetal. |     |     |     |
| ------ | --------- | ------ | ---------- | --- | ---- | --- | ------------------------- | --- | --- | ------------------- | --- | --- | --- |
| ing of | Grounding | Guided | Generation |     | (G3) | and |                           |     |     |                     |     |     |     |
(2023b)enablesLLMstogeneratetextwithin-line
Consistency-AwareAlignment(CAA).Duringthe
|     |     |     |     |     |     |     | citations | via in-context |     | learning. | Another |     | line of |
| --- | --- | --- | --- | --- | --- | --- | --------- | -------------- | --- | --------- | ------- | --- | ------- |
G3
stage,theLLMfirstselectssupportingquotes
|     |     |     |     |     |     |     | research | (Gao | et al., 2023a; | Xu  | et al., | 2023b) | ex- |
| --- | --- | --- | --- | --- | --- | --- | -------- | ---- | -------------- | --- | ------- | ------ | --- |
fromretrievedsources(grounding)andthencondi-
plorespost-hocattribution,whereLLMsfirstgen-
tionsthegenerationprocessonthem(generation).
erateaninitialresponseandthenretrievethemost
TheCAAstagethenutilizespreferenceoptimiza-
|     |     |     |     |     |     |     | relevantevidencetoachieveattribution. |     |     |     |     | Inthispa- |     |
| --- | --- | --- | --- | --- | --- | --- | ------------------------------------- | --- | --- | --- | --- | --------- | --- |
tiontofurtheralignthegroundingandgeneration
per,weadvancetheresearchonattributedLLMs
processbyautomaticallyconstructingpreference
|     |     |     |     |     |     |     | further. | Unlikeexistingmodelsthatpredominantly |     |     |     |     |     |
| --- | --- | --- | --- | --- | --- | --- | -------- | ------------------------------------- | --- | --- | --- | --- | --- |
signals. Inthisway,thesequotescanserveasfine-
|     |     |     |     |     |     |     | cite document |     | identifiers, | we  | delve | into | a more |
| --- | --- | --- | --- | --- | --- | --- | ------------- | --- | ------------ | --- | ----- | ---- | ------ |
grainedcitationsandimprovetheefficiencyofthe
fine-grainedformofattributionbypinpointingand
verificationprocessforusers(seeFigure1).
citingspecificextractivequotes.
| We conduct |     | extensive | experiments |     | to  | evaluate |     |     |     |     |     |     |     |
| ---------- | --- | --------- | ----------- | --- | --- | -------- | --- | --- | --- | --- | --- | --- | --- |
our framework on the ALCE Benchmark (Gao 3 TaskFormulationandMethodology
| etal.,2023b). | Ourfindingsareasfollows: |     |     |     |     |     |     |     |     |     |     |     |     |
| ------------- | ------------------------ | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
Following(Liuetal.,2023;Gaoetal.,2023b),the
| • FRONT | demonstratessupervisorperformance |     |     |     |     |     |                    |     |             |     |       |        |       |
| ------- | --------------------------------- | --- | --- | --- | --- | --- | ------------------ | --- | ----------- | --- | ----- | ------ | ----- |
|         |                                   |     |     |     |     |     | task is formalized |     | as follows: |     | given | a user | query |
gainsincitationqualitycomparedtoallbase- q and a corpus of retrieved documents D as in-
lines,achievinganaverage14.21%improve-
|     |     |     |     |     |     |     | put, the | LLM | is required | to  | produce | a response |     |
| --- | --- | --- | --- | --- | --- | --- | -------- | --- | ----------- | --- | ------- | ---------- | --- |
mentusingLLaMA-2-7B.
|     |     |     |     |     |     |     | S, which          | consists | of statements        |     | with | embedded |      |
| --- | --- | --- | --- | --- | --- | --- | ----------------- | -------- | -------------------- | --- | ---- | -------- | ---- |
|     |     |     |     |     |     |     | in-linecitations. |          | WeassumetheresponseS |     |      |          | com- |
• Humanevaluationrevealsthatthequotesgen-
|                                          |     |               |     |     |         |         | prising           | with     | n statements | S                       | = {s | ,s ,...,s     | }   |
| ---------------------------------------- | --- | ------------- | --- | --- | ------- | ------- | ----------------- | -------- | ------------ | ----------------------- | ---- | ------------- | --- |
| erated                                   | by  | our framework |     | are | of high | quality |                   |          |              |                         |      | 1 2           | n   |
|                                          |     |               |     |     |         |         | andeachstatements |          |              | ∈ S,citesalistofpassage |      |               |     |
| andsignificantlybenefituserverification. |     |               |     |     |         |         |                   |          | i            |                         |      |               |     |
|                                          |     |               |     |     |         |         | C i = {c          | i1 ,c i2 | ,...}, where | c ij                    | ∈ D. | Specifically, |     |
• AnalysisshowsthatFRONTgenerateslesshal- citationsarepresentedintheformof[1][2].
lucinationanddemonstratesremarkablegen- Next,wepresentacomprehensiveoverviewof
eralizationacrossdifferentbasemodels. ourmethod,whichconsistsoftwoprimarycompo-

Automatic Data Generation Pipeline
          Data Collection        Answer Generation           Data Filtering
Question: Why do bagels have holes in the middle？ Bagel holes ensures even baking (...) a area
|     |     |     |     |     |                                              | Demonstrations |     |     |  1    3  . Another suggests (...) and transport  2  . |     |     |     |
| --- | --- | --- | --- | --- | -------------------------------------------- | -------------- | --- | --- | ----------------------------------------------------- | --- | --- | --- |
|     |     |     |     |     | Instruction: Generate an answer and cite the |                |     |     | Informative?                                          |     |     |     |
Retrieve source for the question and provided documents Break into
Question: Why do bagels have holes in the middle？
|     | Top-100 retrieval documents |     |     |     |     |     |     |     |     |     | statements |     |
| --- | --------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---------- | --- |
(...)
|     |     |     |     |     | Prior to (...) the hole was originally put in place |     |     |     | Statement 1 | Bagel holes ensures even baking by |     |     |
| --- | --- | --- | --- | --- | --------------------------------------------------- | --- | --- | --- | ----------- | ---------------------------------- | --- | --- |
allowing heat to circulate around a
|     |          |                                  |                                    |     | to allow for easier handing prior. |     |     |     |     | greater surface area  1    3  .  |     |     |
| --- | -------- | -------------------------------- | ---------------------------------- | --- | ---------------------------------- | --- | --- | --- | --- | -------------------------------- | --- | --- |
|     | T h e  b | a k e r s   (. .. )   th e   h o | l e  i n  t h e   c e n tre of the |     |                                    |     |     |     |     |                                  |     |     |
1 ba g e l  e n s u r e s   t h a t  i t  b a k e s   e v e n ly . Statement 2 A n o t h e r  s u g g e s t s  t h a t  t h e y  w e re   u s ed
|        |                                               |     |     |     |                                                  |     |     |     |             | fo r  e a s i e r  | h a n d li n g   a n | d   tr a n sp o rt    2   . |
| ------ | --------------------------------------------- | --- | --- | --- | ------------------------------------------------ | --- | --- | --- | ----------- | ------------------ | -------------------- | --------------------------- |
|        | Prior to (...) the hole was originally put in |     |     |     | Bagel holes ensures even baking by allowing      |     |     |     |             |                    |                      |                             |
|        | 2                                             |     |     |     | heat to circulate around a greater surface       |     |     |     |             |                    |                      | Support                     |
|        | place to allow for easier handing prior.      |     |     |     |                                                  |     |     |     | Attributed? |                    |                      |                             |
| Rerank |                                               |     |     |     | area   1   3   . Another suggests that they were |     |     |     |             |                    |                      |                             |
T h e  r i n g   s h a p e  a l lo w s   h e a t  t o   c ir c u la t e used for easier handling and transport  2  . Documents
|     | 3 ar o u n | d   ( .. . )  m a k e s  b a g | e l s  t o   c o o k   fa s t er  |     |     |     |     |     |     |     |     |     |
| --- | ---------- | ------------------------------ | --------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
Figure2: Overviewofthedatagenerationpipeline. Thepipelineconsistsofthreeprimarysteps: datacollection,
answergeneration,anddatafiltering. Firstly,givenauserquery,thedatacollectionmoduleretrievesthetop100
relevantdocumentsandemploysarerankingmodeltoselectthetop5mostpertinentdocuments. Subsequently,
attributedresponsesaregeneratedbydistillingChatGPTviain-contextlearning. Finally,allresponsesarefiltered
bythedatafilteringmoduletoensureinformativenessandattributability.
nents: anautomaticdatagenerationpipeline(§3.1) subsequently re-rankedby RankVicuna (Pradeep
andatwo-stagetrainingframework(§3.2). etal.,2023)consideringitssuperiorperformance
|     |     |     |     |     |     | inlistwisere-ranking, |     |     | resultinginthetop5most |     |     |     |
| --- | --- | --- | --- | --- | --- | --------------------- | --- | --- | ---------------------- | --- | --- | --- |
3.1 AutomaticDataGenerationPipeline
relevantdocumentsforeachquery.
EquippingLLMswiththeattributioncapabilityne-
|            |          |      |               |              |          | Attributed | Answer      |     | Generation. |         | Given | the re-    |
| ---------- | -------- | ---- | ------------- | ------------ | -------- | ---------- | ----------- | --- | ----------- | ------- | ----- | ---------- |
| cessitates | training | data | that includes | high-quality |          |            |             |     |             |         |       |            |
|            |          |      |               |              |          | markable   | performance |     | of          | ChatGPT | in    | attributed |
| responses  | paired   | with | precise       | citations,   | which is |            |             |     |             |         |       |            |
questionanswering,weemployChatGPTtogener-
| typically | labor-intensive |     | and | costly. To | address |     |     |     |     |     |     |     |
| --------- | --------------- | --- | --- | ---------- | ------- | --- | --- | --- | --- | --- | --- | --- |
ateanswerswithcorrespondingcitationsforgiven
thischallenge,weproposeapipelinedesignedfor
|     |     |     |     |     |     | queriesandthetop5retrieveddocuments. |     |     |     |     |     | Wepro- |
| --- | --- | --- | --- | --- | --- | ------------------------------------ | --- | --- | --- | --- | --- | ------ |
theautomaticgenerationofhigh-qualityattributed
videpreciseinstructionsandin-contextdemonstra-
data4.
Thispipelinecomprisesthreecorecompo-
tionstoensurethatChatGPTproducesinformative
nents: datacollection,attributedanswergeneration,
responsesandcitesthesourcesaccordingly.
anddatafiltering,asoutlinedinFigure2.
|                 |     |                              |     |     |     | DataFiltering. |     | Toguaranteethehighqualityof |       |           |     |             |
| --------------- | --- | ---------------------------- | --- | --- | --- | -------------- | --- | --------------------------- | ----- | --------- | --- | ----------- |
| DataCollection. |     | Tosimulatethereal-worldenvi- |     |     |     |                |     |                             |       |           |     |             |
|                 |     |                              |     |     |     | our synthetic  |     | training                    | data, | we employ |     | a data fil- |
ronmentforinformation-seeking,wecollectques-
teringprocessguidedbytwokeycriteriaderived
tionsfromtheAQuAMuSedataset(Kulkarnietal.,
|     |     |     |     |     |     | fromKamallooetal.(2023): |     |     |     | (1)informativeness: |     |     |
| --- | --- | --- | --- | --- | --- | ------------------------ | --- | --- | --- | ------------------- | --- | --- |
2020),whichisderivedfromtheNaturalQuestion
|                                     |     |     |     |     |       | assessing                                  | if the | answer | provides |     | sufficient | infor- |
| ----------------------------------- | --- | --- | --- | --- | ----- | ------------------------------------------ | ------ | ------ | -------- | --- | ---------- | ------ |
| (NQ)dataset(Kwiatkowskietal.,2019). |     |     |     |     | TheNQ |                                            |        |        |          |     |            |        |
|                                     |     |     |     |     |       | mationtothequestion,and(2)attributability: |        |        |          |     |            | de-    |
datasetcomprisesrealuserqueriesfromtheGoogle
|     |     |     |     |     |     | termining | if the | answer | is  | attributed |     | to the cited |
| --- | --- | --- | --- | --- | --- | --------- | ------ | ------ | --- | ---------- | --- | ------------ |
searchengine,providingarobustbasisforrealistic
|                    |     |            |     |             |       | documents. | To  | mitigate |     | the impact | of  | nonsensi- |
| ------------------ | --- | ---------- | --- | ----------- | ----- | ---------- | --- | -------- | --- | ---------- | --- | --------- |
| question-answering |     | scenarios. |     | The dataset | spans |            |     |          |     |            |     |           |
calqueriesandirrelevantdocumentretrievalthat
| a range | of diverse | question | types, | demanding | an- |          |                    |     |     |          |     |            |
| ------- | ---------- | -------- | ------ | --------- | --- | -------- | ------------------ | --- | --- | -------- | --- | ---------- |
|         |            |          |        |           |     | may lead | to non-informative |     |     | answers, |     | we utilize |
swersofvaryinglengths,fromconcisetodetailed.
ChatGPTforpreliminaryinformativenessannota-
Tomimicthewayasearchenginemightsynthesize
|     |     |     |     |     |     | tions. Responses |     | categorized |     | as  | non-informative |     |
| --- | --- | --- | --- | --- | --- | ---------------- | --- | ----------- | --- | --- | --------------- | --- |
documentsofhighrelevanceinresponsetoauser
|        |           |        |         |         |          | aredirectlyexcluded. |     |     | Furthermore,toensurethat |     |     |     |
| ------ | --------- | ------ | ------- | ------- | -------- | -------------------- | --- | --- | ------------------------ | --- | --- | --- |
| query, | we employ | Sphere | (Piktus | et al., | 2021), a |                      |     |     |                          |     |     |     |
answersareaccompaniedbyhighlysupportiveci-
pre-processedandcleanedversionoftheCommon
tations,wetrainadiscriminatoronhuman-labeled
Crawlcorpus,servingasaproxywebsearchindex.
|     |     |     |     |     |     | data from | the | comprehensive |     | evaluation |     | by Liu |
| --- | --- | --- | --- | --- | --- | --------- | --- | ------------- | --- | ---------- | --- | ------ |
Inparticular,foragivenuserquerysampledfrom
|     |              |            |              |            |      | et al. (2023),   |                                       | where                           | attributability |       | is  | categorized |
| --- | ------------ | ---------- | ------------ | ---------- | ---- | ---------------- | ------------------------------------- | ------------------------------- | --------------- | ----- | --- | ----------- |
| the | AQuAMuSe     | dataset,   | we initially | retrieve   | the  |                  |                                       |                                 |                 |       |     |             |
|     |              |            |              |            |      | intothreelevels: |                                       | fullsupport,partialsupport,orno |                 |       |     |             |
| top | 100 relevant | documents  | from         | the Sphere | cor- |                  |                                       |                                 |                 |       |     |             |
|     |              |            |              |            |      | support.         | Wequantitativelymapthediscriminator’s |                                 |                 |       |     |             |
| pus | using sparse | retrieval. | These        | documents  | are  |                  |                                       |                                 |                 |       |     |             |
|     |              |            |              |            |      | outputs          | to an                                 | attributability                 |                 | score | and | ultimately  |
4Attributeddatarefersto“answerswithin-linecitations”. deriveanaveragescoreforeachattributedanswer.

Two-Stage Training Recipe
          Grounding Guided Generation        Consistency-Aware Alignment
          Answer Generation
| Q :   W h y   d o |   b a g e l s   h a v e |                  |                             |                         |                                       |     |                         |                     |                                                 |                             |     |     |
| ----------------- | ----------------------- | ---------------- | --------------------------- | ----------------------- | ------------------------------------- | --- | ----------------------- | ------------------- | ----------------------------------------------- | --------------------------- | --- | --- |
|                   |                         | G r o u n d i ng | T h e   b a k e r s   ( . . | . )   t h e   h o l e   | i n   t h e   c e n t r e   o f   the | Q   | u e s t i o n :   W h y |   d o   b a g e l s |   G   R            D    eI     G       1l     e | n s u r e s   t h a t   i t |     |     |
h o l e s   i n   t h e   m i d d l e ？ 1 b a g e l  { e n s u r e s   t h a t   i t   b a k e s   e v e n l y } . h a v e   h o l e s   i n   t h e   m i d d l e ？ b a kO eU Ns N v e n y .    3     a l l o w s   h e a t
|     |     |     |     |     |     |     | ( . . . ) |     | t o   c i r c u l a t e   a r | o u n d .   2    a l l o w   s A  u   N  r S    f W  a    c E |   R e        a B r a e g a e    1 l       h  3  o  A l e n s o  ( t . h . . e )   r | g   ( r . e .. a ) t |
| --- | --- | --- | --- | --- | --- | --- | --------- | --- | ----------------------------- | ------------------------------------------------------------- | ----------------------------------------------------------------------------------- | -------------------- |
B a g e l  h o l e s   e n s u r e s   e v e n   b a k i n g   b y P r i o r   t o   ( . . . )   t h e   h o l e   w a s   o r i g i n a l l y   p u t   in P r i o r   t o   ( .. . )   p l a c e   t o   a ll o w f o r   e a s i e r   h a n d i n g   p r i o r . h a n d l i n g  a n d   t r a n s p o r t     2    .
| al lo w i n g  h e a | t   to   c i r c u l a t e |   a r o u n d   a 2 |     |     |     |     |     |     |     |     |     |     |
| -------------------- | -------------------------- | ------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
greater surface area  1   3  .Another p l a c e   t o   { a l l o w   f o r  e a s ie r   h a n d i n g   p r i o r } . fo r   e a s i e r   h a n d i n g   p r i o r . Consistency
| suggests that they were used for |     | 3   | The ring shape {allows heat to circulate |     |     |     |     |     |     |     |     |     |
| -------------------------------- | --- | --- | ---------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
easier handling and transport  2  . around} (...) makes bagels to cook faster  Gold Grounding Consistent Answer Contrastive
Large Language
|          | Supervised Fine-tuning |                |               |                                               |                                                   |     | Model |     |                |                     |     |     |
| -------- | ---------------------- | -------------- | ------------- | --------------------------------------------- | ------------------------------------------------- | --- | ----- | --- | -------------- | ------------------- | --- | --- |
|          |                        |                |               |                                               |                                                   |     |       |     | Gold Grounding | Inconsistent Answer |     |     |
| Question |                        |                |     G   R     | Ol    Uo  N   D  sI  N    Gh        1   e n s | u r e s   t h a t   it   b a k e s   e v e n ly.  |     |       |     |                |                     |     |     |
|          |                        | Large Language |   f3   a      | l w e a t   t o                               | c i r c u l a t e   a r o u n d .    2  a l lo w  |     |       |     |                |                     |     |     |
|          |                        |                | o r           | e a s i e r   h a n d i n                     | g   p r i o r .                                   |     |       |     |                |                     |     |     |
Documents Model Direct Preference Optimization (DPO)   A    N   S    W      E   R        B a g e l  h o l e s   (. . . )  f o r
|     |     |     |   A A  N n   S o |  W  t   h  E  e  R  r     B (. a .. g )  e h l a  h n o d le | lin s g  (   . a .. n )  d g   r t e ra a n t  s s p u o rf r a t  c  2  e  .  area13 |     |     |     |     | a e s t h e       | t i c s   a n d   u n i q u e   t e x | t u r e   1   |
| --- | --- | --- | ---------------- | ------------------------------------------------------------ | ------------------------------------------------------------------------------------- | --- | --- | --- | --- | ----------------- | ------------------------------------- | ------------- |
|     |     |     |                  |                                                              |                                                                                       |     |     |     |     |   ( . . . )   h a | n d l i n g   a n d   tr a n s p o    | r t    3    . |
Figure 3: Overview of FRONT: The training recipe consists of two stages: grounding-guided generation and
consistency-aware alignment. It enables LLMs to first generate precise grounding and subsequently guide the
generationofattributedanswers,therebyenhancingfine-grainedattributioncapability.
Answersfallingbelowadefinedthresholdaresys- fine-tunedtogeneratearesponseS whichconsists
tematicallyexcludedtoensurethesyntheticdata’s of two components: the grounded quotes G and
reliability, which results in nearly 8,000 entries. theattributedanswerA. Specifically,thegrounded
Formoredetails,pleaserefertoAppendixA. quotesG aredelineatedasfollows:
3.2 Two-StageTrainingRecipe G = {[GROUNDING],(i ,e ),...,(i ,e )}, (1)
|                           |     |     |                  |     |     |     |     |     |     | 1 1 | n n |     |
| ------------------------- | --- | --- | ---------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Inthissection,weintroduce |     |     | FRONT,atwo-stage |     |     |     |     |     |     |     |     |     |
where[GROUNDING]denotesaspecialtokenindi-
trainingframeworkthataimsatempoweringLLMs
|                   |     |             |             |     |          | cating | the | start of | the grounding | process. |     | Each |
| ----------------- | --- | ----------- | ----------- | --- | -------- | ------ | --- | -------- | ------------- | -------- | --- | ---- |
| with fine-grained |     | attribution | capability. |     | Figure 3 |        |     |          |               |          |     |      |
tuplewithinG,comprisingadocumentidentifieri
illustratestheoverviewofourframework.
andthecorrespondingextractivesegmente,collec-
tivelyformingagroundedquote.
3.2.1 GroundingGuidedGeneration
|            |     |           |              |     |          | Similarly, |     | the formulation |     | of the | attributed | an- |
| ---------- | --- | --------- | ------------ | --- | -------- | ---------- | --- | --------------- | --- | ------ | ---------- | --- |
| To empower |     | LLMs with | fine-grained |     | attribu- |            |     |                 |     |        |            |     |
swerAisconciselypresentedas:
| tion capability, |     | we propose | Grounding |     | Guided |     |     |     |     |     |     |     |
| ---------------- | --- | ---------- | --------- | --- | ------ | --- | --- | --- | --- | --- | --- | --- |
(G3),
Generation which teaches LLMs to gener- A = {[ANSWER],s ,s ...,s }, (2)
|                           |     |     |                    |     |     |     |     |     |     | 1 2 | m   |     |
| ------------------------- | --- | --- | ------------------ | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| atefine-grainedcitations. |     |     | ThecornerstoneofG3 |     |     |     |     |     |     |     |     |     |
liesinenablingLLMstoextractsupportingquotes where [ANSWER] is a special token that signals
fromthesourcedocuments,eachassociatedwithits the beginning of the answer generation process.
documentidentifier,whichinturnguidesthegen- Each statement s cites a list of passages C =
|                             |     |                             |                   |     |     |                   |              | i      |     |                    |      | i     |
| --------------------------- | --- | --------------------------- | ----------------- | --- | --- | ----------------- | ------------ | ------ | --- | ------------------ | ---- | ----- |
| erationofattributedanswers. |     |                             | Suchaformatoffers |     |     |                   |              |        |     |                    |      |       |
|                             |     |                             |                   |     |     | {c i1             | ,c i2 ,...}, | wherec | ij  | ⊆ {i 1 ,i 2 ,...,i | n }, | asde- |
| twoprimarybenefits.         |     | Firstly,thedirectextraction |                   |     |     | finedinEquation2. |              |        |     |                    |      |       |
of quotes from sources significantly reduces the Thus,thetraininglossisformulatedas:
impactoftheincorporationofirrelevantinforma-
| tion and | the risk | of hallucinations |     | in  | subsequent |     |     | N   |     |     |     |     |
| -------- | -------- | ----------------- | --- | --- | ---------- | --- | --- | --- | --- | --- | --- | --- |
(cid:88)
|                    |     |                              |     |     |     |     |     | L = − | logP(y | |q ,D | ;θ) | (3) |
| ------------------ | --- | ---------------------------- | --- | --- | --- | --- | --- | ----- | ------ | ----- | --- | --- |
| attributedanswers. |     | Secondly,theprocessnaturally |     |     |     |     |     |       |        | i i   | i   |     |
i=1
facilitatesaccurateattribution,witheachdocument
identifierservingasaclearsupervisedsignalthat
|     |     |     |     |     |     | where | y   | represents | the | combined | output | of  |
| --- | --- | --- | --- | --- | --- | ----- | --- | ---------- | --- | -------- | ------ | --- |
i
delineatestheoriginoftheextractivequotes,thus
groundedquotesGandtheanswerAforeachgiven
improvingthecitationquality. queryq andsetofretrieveddocumentsD .
|     |     |     |     |     |     |     | i   |     |     |     | i   |     |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
However,theabsenceofspecificgroundingcon-
tent for statements within our generated dataset 3.2.2 Consistency-AwareAlignment
posesadditionalchallenges. Totacklethis,weem- While G3 unlocks the ability to first extract sup-
ploy ChatGPT to meticulously extract segments portingquotesbeforegeneratingattributedanswers,
fromciteddocumentsthatsupportthecorrespond- it occasionally leads to inconsistencies between
ingstatement. Hence,whengivenaqueryqandthe groundedquotesandattributedanswers. Suchdis-
top-5retrieveddocumentsD asinput,theLLMis crepancieschallengetheattempttoemploythese

groundedquotesasfine-grainedverification. Inre- whereπ ref representsthereferencemodel,initial-
sponse,weproposeaconsistency-awarealignment izedfromG3. Thehyper-parameterβ modulates
(CCA) stage specifically aimed at enhancing the the divergence between the distribution from the
consistencybetweenthegroundingprocessandthe policy model and the reference model. τ is the
w
generationprocess. consistentanswer,whileτ istheinconsistentone.
l
|     | The cornerstone |     | of  | our approach | involves | con- |     |     |     |     |     |     |     |
| --- | --------------- | --- | --- | ------------ | -------- | ---- | --- | --- | --- | --- | --- | --- | --- |
4 ExperimentalSettings
| trasting        |     | a consistent |                            | answer   | with an     | inconsis- |              |     |     |     |     |     |     |
| --------------- | --- | ------------ | -------------------------- | -------- | ----------- | --------- | ------------ | --- | --- | --- | --- | --- | --- |
| tent            | one | under        | the                        | guidance | of the same | oracle    | 4.1 Datasets |     |     |     |     |     |     |
| groundedquotes. |     |              | Thisalignswiththeconceptof |          |             |           |              |     |     |     |     |     |     |
WeconductexperimentsontheALCEbenchmark
| Reinforcement |     |     | Learning | from | Human | Feedback |     |     |     |     |     |     |     |
| ------------- | --- | --- | -------- | ---- | ----- | -------- | --- | --- | --- | --- | --- | --- | --- |
(Gaoetal.,2023b),designedforattributedtextgen-
| (RLHF) |     | (Ouyang | et  | al., 2022; | Bai et | al., 2022), |     |     |     |     |     |     |     |
| ------ | --- | ------- | --- | ---------- | ------ | ----------- | --- | --- | --- | --- | --- | --- | --- |
eration. Thebenchmarkincludesthreelong-form
whereLLMsarefurtherfine-tunedtodistinguish
QAdatasetsthatspanvarioustypesofquestions.
betweendesirableandundesirableresponsesunder
preference feedback. However, such contrastive ASQA (Stelmakh et al., 2022) is a long-form
preferencefeedbacktypicallycomesfromhuman factoidQAdatasetcharacterizedbyinherentlyam-
| annotation. |     | Toautomaticallyconstructpreference |     |     |     |     |         |           |      |         |          |       |     |
| ----------- | --- | ---------------------------------- | --- | --- | --- | --- | ------- | --------- | ---- | ------- | -------- | ----- | --- |
|             |     |                                    |     |     |     |     | biguous | questions | that | require | multiple | short | an- |
pairsforpreferenceoptimization,weutilizetheat- swerstoencapsulatedifferentviewpoints.
tributedanswersgeneratedbysmallerLLMs(e.g.,
LLaMA-2-7B)underthein-contextlearningsetting ELI5 (Fanetal.,2019)featuresopen-endedques-
tionsintendedforsimplificationtothecomprehen-
| asnegativesamples. |     |     |     | Theseanswers,characterized |     |     |     |     |     |     |     |     |     |
| ------------------ | --- | --- | --- | -------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
sionleveloffive-year-olds,requiringexplanatory
bytheirlowqualityandinconsistencywithoracle
multi-sentenceresponses.
groundedquotes,automaticallyserveascontrastive
supervisionsignalswhenpairedwithhigh-quality
|                                 |     |     |     |     |                 |     | QAMPARI |     | (Amouyaletal.,2022)isafactoid |     |     |     |     |
| ------------------------------- | --- | --- | --- | --- | --------------- | --- | ------- | --- | ----------------------------- | --- | --- | --- | --- |
| attributedanswerslabeledin§3.1. |     |     |     |     | Inthisscenario, |     |         |     |                               |     |     |     |     |
QAdatasetderivedfromWikipedia,whereanswers
theprocessnotonlyencouragestheLLMtogener- arestructuredasacompilationofentities.
ateattributedanswersthataremoreconsistentwith
thegroundedquotesbutalsofacilitatestheidentifi- 4.2 EvaluationMetrics
cationandcorrectionofnuancederrorspresentin Following the ALCE benchmark (Gao et al.,
smallermodels. 2023b), our evaluation primarily focuses on two
|     | Specifically, |     | we adopt | Direct | Preference | Opti- |                 |     |          |     |         |              |     |
| --- | ------------- | --- | -------- | ------ | ---------- | ----- | --------------- | --- | -------- | --- | ------- | ------------ | --- |
|     |               |     |          |        |            |       | key dimensions: |     | Citation |     | Quality | and Correct- |     |
mization(Rafailovetal.,2023),avariantofRLHF ness. Detaileddescriptionsofadditionalevaluation
known for its stability, for our contrastive align- dimensionsarepresentedintheAppendixB.
ment. Formally,foreachinstance,giventheoracle
|     |     |     |     |     |     |     | CitationQuality. |     | Citationqualityiscriticalfor |     |     |     |     |
| --- | --- | --- | --- | --- | --- | --- | ---------------- | --- | ---------------------------- | --- | --- | --- | --- |
groundedg(i)alongwithaconsistentoracleanswer
| (i) |                             |     |     |     | (i) |           | evaluating  | LLM | attribution, |         | assessed    | along | two |
| --- | --------------------------- | --- | --- | --- | --- | --------- | ----------- | --- | ------------ | ------- | ----------- | ----- | --- |
| y   | aswellasanattributedanswery |     |     |     |     | generated |             |     |              |         |             |       |     |
| w   |                             |     |     |     | l   |           |             |     |              |         |             |       |     |
|     |                             |     |     |     |     |           | dimensions: | (1) | Citation     | Recall, | determining |       | if  |
byaweakerLLMviain-contextlearning,wecan
|     |     |     |     |     |     |     | the output | is entirely |     | supported | by  | the cited | doc- |
| --- | --- | --- | --- | --- | --- | --- | ---------- | ----------- | --- | --------- | --- | --------- | ---- |
simplyconstructapreferencedataset:
|     |     |     |                       |     |               |     | uments,                                        | and (2) | Citation | Precision, |     | assessing | if  |
| --- | --- | --- | --------------------- | --- | ------------- | --- | ---------------------------------------------- | ------- | -------- | ---------- | --- | --------- | --- |
|     |     | D   | = (cid:8) x(i),τ(i),τ |     | (i)(cid:9)N , |     |                                                |         |          |            |     |           |     |
|     |     |     |                       | w   |               | (4) | eachcitationsupportsitscorrespondingstatement. |         |          |            |     |           |     |
l i=1
|        | (i) |          | (i) |                           |     |     | EvaluationisconductedbyTRUE(Honovichetal., |     |     |     |     |     |     |
| ------ | --- | -------- | --- | ------------------------- | --- | --- | ------------------------------------------ | --- | --- | --- | --- | --- | --- |
| whereτ |     | = g(i)◦y |     | denotestheconcatenationof |     |     |                                            |     |     |     |     |     |     |
|        | w   |          | w   |                           |     |     | 2022),aT5-11Bmodelfine-tunedonacollection  |     |     |     |     |     |     |
theoraclegroundingwiththeconsistent,attributed
ofNLIdatasetstoautomaticallyexaminetheentail-
|          |     | (i)          | g(i)◦y | (i)                     |         |         |                                            |     |         |            |         |     |        |
| -------- | --- | ------------ | ------ | ----------------------- | ------- | ------- | ------------------------------------------ | --- | ------- | ---------- | ------- | --- | ------ |
| answer,τ |     | =            |        | denotestheconcatenation |         |         |                                            |     |         |            |         |     |        |
|          |     | l            |        | l                       |         |         | mentofciteddocumentsandthemodelgeneration. |     |         |            |         |     |        |
| with     | the | inconsistent |        | attributed              | answer. | Here, ◦ |                                            |     |         |            |         |     |        |
|          |     |              |        |                         |         |         | Additionally,                              | to  | capture | a holistic | measure |     | of ci- |
signifiestheoperationofstringconcatenation.
tationquality,wealsoreporttheCitationF1,the
|     | Finally,wecanoptimizethepolicymodelπ |     |     |     |     | on  |                                           |     |     |     |     |     |     |
| --- | ------------------------------------ | --- | --- | --- | --- | --- | ----------------------------------------- | --- | --- | --- | --- | --- | --- |
|     |                                      |     |     |     |     | θ   | harmonicmeanofcitationprecisionandrecall: |     |     |     |     |     |     |
thedatasetD
byminimizingthefollowingloss:
citationprecision·citationrecall
|     | L   | (π ;π | ;D) |     |     |     |          |     |     |     |     |     |       |
| --- | --- | ----- | --- | --- | --- | --- | -------- | --- | --- | --- | --- | --- | ----- |
|     | DPO | θ     | ref |     |     |     | F 1 = 2· |     |     |     |     |     | , (6) |
citationprecision+citationrecall
|     |     |     | (cid:20) | (cid:18) | π (τ | |x) |     |     |     |     |     |     |     |
| --- | --- | --- | -------- | -------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- |
|     | =−E |     |          |          | θ    | w   |     |     |     |     |     |     |     |
|     |     |     |          | logσ     | βlog |     |     |     |     |     |     |     |     |
(x,τw,τ l )∼D π (τ |x) (5) Correctness. FortheASQAdataset,correctness
ref w
π (τ |x) (cid:19)(cid:21) is quantified using exact match recall (EM Rec.)
θ l
|     | −βlog |     |        | ,   |     |     |             |         |     |           |         |     |       |
| --- | ----- | --- | ------ | --- | --- | --- | ----------- | ------- | --- | --------- | ------- | --- | ----- |
|     |       | π   | (τ |x) |     |     |     | by checking | whether |     | the short | answers | are | exact |
|     |       |     | ref l  |     |     |     |             |         |     |           |         |     |       |

|           |     |             | ASQA |           |             | ELI5 |          | QAMPARI      |            |     |
| --------- | --- | ----------- | ---- | --------- | ----------- | ---- | -------- | ------------ | ---------- | --- |
| ModelType |     | ModelSize   |      |           |             |      |          |              |            |     |
|           |     | Correctness |      | Citation  | Correctness |      | Citation | Correctness  | Citation   |     |
|           |     | EMRec.      | Rec. | Prec. F1. | Claim       | Rec. | Prec. F1 | Rec.-5 Prec. | Rec. Prec. | F1  |
Prompting-based
ChatGPT - 40.37 72.81 69.69 71.22 12.47 49.44 47.05 48.22 20.28 19.84 19.06 22.03 20.44
7B 24.32 17.24 17.87 17.55 4.53 3.92 5.38 4.54 12.56 11.32 6.03 6.35 6.19
LLaMA-2 13B 27.99 16.45 19.04 17.65 7.77 8.49 8.43 8.46 18.00 12.39 5.45 5.74 5.59
70B 31.53 44.18 44.79 44.48 10.43 23.75 22.43 23.07 18.50 14.79 10.10 10.50 10.30
7B 29.93 55.99 51.66 53.74 12.47 19.90 15.48 17.41 17.96 19.74 9.58 9.68 9.63
LLaMA-2-Chat 13B 34.39 37.15 38.17 37.65 13.83 16.50 16.09 16.29 21.34 18.86 8.94 9.06 9.00
70B 41.24 60.19 61.16 60.67 13.30 36.63 36.63 36.63 22.62 18.04 13.49 13.98 13.73
Vicuna-v1.5 7B 38.34 48.37 44.63 46.42 12.30 29.81 22.45 25.61 14.22 14.74 11.26 11.64 11.45
13B 35.20 51.92 53.40 52.65 14.33 31.15 28.99 30.03 22.06 19.60 13.04 13.74 13.38
7B 29.46 23.12 25.45 24.23 8.47 16.04 16.32 16.18 16.96 15.98 7.50 7.76 7.63
Mistral
8×7B 36.30 32.72 34.49 33.58 10.43 26.11 25.09 25.59 18.18 15.63 9.72 10.20 9.95
7B 38.57 64.90 59.67 62.18 11.07 49.25 42.69 45.74 17.52 21.29 17.56 18.53 18.03
Mistral-Instruct 8×7B 44.11 61.80 63.27 62.53 13.93 49.28 48.34 48.81 20.12 19.64 19.27 20.38 19.81
Post-hocRetrieval
ChatGPT - 37.68 27.11 27.05 27.08 18.77 14.55 14.55 14.55 25.14 22.85 12.29 12.29 12.29
LLaMA-2-Chat 70B 29.68 24.51 24.51 24.51 16.03 12.93 12.93 12.93 17.90 14.45 9.05 9.05 9.05
Mistral-Instruct 8×7B 33.90 24.57 24.48 24.52 17.37 15.68 15.68 15.68 24.16 18.28 9.78 9.78 9.78
Training-based
7B 29.96 67.82 66.97 67.39 6.90 22.34 32.40 26.45 2.34 1.98 10.53 18.80 13.50
Self-RAG(LLaMA-2)
13B 31.66 71.26 70.35 70.80 6.07 30.46 40.20 34.66 1.90 1.33 12.79 20.90 15.86
7B 40.32 67.67 63.67 65.61 9.63 42.30 40.06 41.15 12.86 21.09 21.35 21.36 21.35
VANILLA-SFT(LLaMA-2) 13B 40.85 71.49 66.21 68.75 10.27 46.75 44.47 45.58 12.68 22.80 23.64 23.71 23.67
FRONT(LLaMA-2) 7B 40.84 77.70 69.89 73.59 9.18 58.60 55.33 56.92 11.50 21.38 24.74 24.84 24.79
13B 41.51 78.44 73.66 75.97 9.32 60.31 59.21 59.75 11.94 22.61 24.86 25.39 25.12
Table1: MainresultsontheALCEbenchmark. Boldnumbersindicatethebestperformance,while_indicatesthe
second-bestperformance.
substringsofthegeneration. RegardingtheELI5 Mistral-7B(Jiangetal.,2023)toMistral-8x7B-
dataset,correctnessismeasuredthroughclaimre- MoE (Jiang et al., 2024). Regarding SFT LLMs,
call (Claim), evaluating whether the model’s re- weselecttheSFTcounterpartsoftheopen-source
sponse entails the ground truth sub-claims. For foundationalLLMsweused. Detailedprompting
theQAMPARIdataset,correctnessisassessedus- settingscanbefoundinAppendixC.
| ingexactmatchprecision(Prec.) |     |     | andtop-5exact |     |     |     |     |     |     |     |
| ----------------------------- | --- | --- | ------------- | --- | --- | --- | --- | --- | --- | --- |
match recall (Rec.-5) — considered 100% if the 4.3.2 Post-hocRetrievalMethods.
predictionincludesatleastfivecorrectanswers.
|     |     |     |     |     | Following | Gao | et al. | (2023b), | we first | instruct |
| --- | --- | --- | --- | --- | --------- | --- | ------ | -------- | -------- | -------- |
LLMstoanswerthegivenqueryinaclosed-book
4.3 Baselines
|     |     |     |     |     | setting, | and then | integrate | citations | in a post-hoc |     |
| --- | --- | --- | --- | --- | -------- | -------- | --------- | --------- | ------------- | --- |
Wecompareourmethodwiththreetypesofbase-
|        |                  |          |            |     | manner. | Foreachgeneratedstatement,weemploy |     |     |     |     |
| ------ | ---------------- | -------- | ---------- | --- | ------- | ---------------------------------- | --- | --- | --- | --- |
| lines: | prompting-based, | post-hoc | retrieval, | and |         |                                    |     |     |     |     |
GTR(Nietal.,2022)toidentifyandcitethemost
training-based.
relevantdocumentfromthetop100retrieveddoc-
4.3.1 Prompting-basedMethods. uments. Weutilizethesamemodelsmentionedin
WedirectlypromptLLMsusingfew-shotdemon- prompting-basedsettingsforthisbaseline.
strations,eachconsistingofaquery,thetop5rel-
|     |     |     |     |     | 4.3.3 | Training-basedMethods. |     |     |     |     |
| --- | --- | --- | --- | --- | ----- | ---------------------- | --- | --- | --- | --- |
evantretrieveddocuments,andananswerwithin-
| linecitations. | Ourexperimentsencompassarange |     |     |     |          |                                   |     |     |     |     |
| -------------- | ----------------------------- | --- | --- | --- | -------- | --------------------------------- | --- | --- | --- | --- |
|                |                               |     |     |     | Self-RAG | (Asaietal.,2023)Self-RAGtrainsthe |     |     |     |     |
ofLLMs,fromfoundationalmodelstosupervised
|                       |     |                      |     |     | LLM | to learn | to adaptively | retrieve | passages | on- |
| --------------------- | --- | -------------------- | --- | --- | --- | -------- | ------------- | -------- | -------- | --- |
| fine-tuning(SFT)LLMs. |     | ForfoundationalLLMs, |     |     |     |          |               |          |          |     |
demandandenableittoreflectonitsgenerationto
| we select | GPT-3.5-Turbo5 | as the | representative |     |     |     |     |     |     |     |
| --------- | -------------- | ------ | -------------- | --- | --- | --- | --- | --- | --- | --- |
furtherimprovegenerationqualityandattributions.
closed-sourcemodel,recognizedforitsnotableper-
| formance. | Among | the open-source | foundational |     |             |     |                            |     |     |     |
| --------- | ----- | --------------- | ------------ | --- | ----------- | --- | -------------------------- | --- | --- | --- |
|           |       |                 |              |     | VANILLA-SFT |     | Wedirectlyemploysupervised |     |     |     |
LLMs,wefocusontheLLaMA-2seriesincluding
fine-tuningtotraintheLLMonourgeneratedtrain-
LLaMA2-7B,LLaMA2-13B,andLLaMA2-70B,
|                                                  |                |               |       |      | ing data.             | Given   | a query | and corresponding |          | doc-     |
| ------------------------------------------------ | -------------- | ------------- | ----- | ---- | --------------------- | ------- | ------- | ----------------- | -------- | -------- |
| as well                                          | as the Mistral | series, which | spans | from |                       |         |         |                   |          |          |
|                                                  |                |               |       |      | uments,               | the LLM | is      | required to       | directly | generate |
| 5Specifically,weutilizegpt-3.5-turbo-1106version |                |               |       |      | answerswithcitations. |         |         |                   |          |          |

4.4 ImplementDetails
| Weimplement |     | FRONTwithdifferentsizesoffoun- |     |     |     |     | 60  |     |     |     | Pre-filtered |     |     |
| ----------- | --- | ------------------------------ | --- | --- | --- | --- | --- | --- | --- | --- | ------------ | --- | --- |
Post-filtered
| dational | models | (LLaMA-2-7B |     | and | LLaMA-2- |     |     |     |     |     |     |     |     |
| -------- | ------ | ----------- | --- | --- | -------- | --- | --- | --- | --- | --- | --- | --- | --- |
)%(1FnoitatiC 50
| 13B)toevaluateitseffectiveness. |     |     |     | Duringtheeval- |     |     |     |     |     |     |     |     |     |
| ------------------------------- | --- | --- | --- | -------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
40
uation,FRONTutilizethesameretrievalsettingsas
| those outlined |             | by Gao | et al.     | (2023b). | Additional |        | 30  |     |     |     |     |     |     |
| -------------- | ----------- | ------ | ---------- | -------- | ---------- | ------ | --- | --- | --- | --- | --- | --- | --- |
| details        | of training | and    | evaluation | settings |            | can be | 20  |     |     |     |     |     |     |
foundinAppendixD.
|     |     |     |     |     |     |     |     | ASQA |     | ELI5 |     | QAMPARI |     |
| --- | --- | --- | --- | --- | --- | --- | --- | ---- | --- | ---- | --- | ------- | --- |
5 ResultsandAnalysis
|     |     |     |     |     |     |     | Figure4: | AblationStudyonDataFiltering. |     |     |     |     |     |
| --- | --- | --- | --- | --- | --- | --- | -------- | ----------------------------- | --- | --- | --- | --- | --- |
5.1 OverallResults
Simplysupervisedfine-tuningcanboostcitation
|             |                                |     |                |     |            |       | FRONT      | demonstrates |     | remarkable |     | generaliza- |         |
| ----------- | ------------------------------ | --- | -------------- | --- | ---------- | ----- | ---------- | ------------ | --- | ---------- | --- | ----------- | ------- |
| quality.    | AsshowninTable1,teachingLLMsto |     |                |     |            |       |            |              |     |            |     |             |         |
|             |                                |     |                |     |            |       | tion.      | Compared     | to  | the varied |     | queries     | and an- |
| generate    | responses                      |     | with citations | via | supervised |       |            |              |     |            |     |             |         |
|             |                                |     |                |     |            |       | swer types | present      | in  | the ALCE   |     | benchmark,  | our     |
| fine-tuning | significantly                  |     | enhances       |     | citation   | qual- |            |              |     |            |     |             |         |
trainingdata,derivedexclusivelyfromtheAQuA-
ity,demonstratingsubstantialimprovementsover
MuSedataset(Kulkarnietal.,2020),exhibitsout-
bothprompt-basedandpost-hocretrievalbaselines
|        |               |     |               |      |          |     | of-domain    | characteristics. |     |          | Nonetheless, |     | FRONT     |
| ------ | ------------- | --- | ------------- | ---- | -------- | --- | ------------ | ---------------- | --- | -------- | ------------ | --- | --------- |
| across | all datasets. |     | Specifically, | with | LLaMA-2- |     |              |                  |     |          |              |     |           |
|        |               |     |               |      |          |     | demonstrates | superior         |     | citation | quality,     |     | affirming |
7B, VANILLA-SFTledtosubstantialgainsincita-
itsexceptionalabilitytogeneralizeacrossdiverse
| tion F1 | scores | over | prompting: | ASQA | (17.55 | →   |                                  |     |     |     |     |               |     |
| ------- | ------ | ---- | ---------- | ---- | ------ | --- | -------------------------------- | --- | --- | --- | --- | ------------- | --- |
|         |        |      |            |      |        |     | querytypesandretrievaldocuments. |     |     |     |     | Additionally, |     |
65.61),ELI5(4.54→41.15),andQAMPARI(6.19
|          |                                     |     |     |     |     |     | while not | specifically   |     | optimized |              | for correctness, |     |
| -------- | ----------------------------------- | --- | --- | --- | --- | --- | --------- | -------------- | --- | --------- | ------------ | ---------------- | --- |
| →21.35). | Thesegainshighlighttheeffectiveness |     |     |     |     |     |           |                |     |           |              |                  |     |
|          |                                     |     |     |     |     |     | FRONT     | also showcases |     | modest    | improvements |                  | in  |
ofourtrainingdatagenerationpipeline.
thismetricoverVANILLA-SFTontheASQAand
|     |     |     |     |     |     |     | QAMPARIdatasets. |     |     | However,FRONTencounters |     |     |     |
| --- | --- | --- | --- | --- | --- | --- | ---------------- | --- | --- | ----------------------- | --- | --- | --- |
FRONT achievessignificantperformancegains
lowerRec.-5ontheQAMPARIdataset,likelydue
| andsurpassesChatGPT. |     |     |     | While VANILLA-SFT |     |     |     |     |     |     |     |     |     |
| -------------------- | --- | --- | --- | ----------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
tothenatureofitsanswers,whichconsistofcon-
| demonstrates |     | strong | performance, |     | it still | shows |     |     |     |     |     |     |     |
| ------------ | --- | ------ | ------------ | --- | -------- | ----- | --- | --- | --- | --- | --- | --- | --- |
catenatedentities,divergingsignificantlyfromour
| notable | discrepancies |     | compared | to  | leading | open- |     |     |     |     |     |     |     |
| ------- | ------------- | --- | -------- | --- | ------- | ----- | --- | --- | --- | --- | --- | --- | --- |
trainingdata.
sourceLLMs,suchasMixtral-8×7B-Instruct(e.g.,
| 41.15 vs. | 45.74) | and  | ChatGPT  | (e.g.,      | 41.15 | vs.      |                   |     |     |     |     |     |     |
| --------- | ------ | ---- | -------- | ----------- | ----- | -------- | ----------------- | --- | --- | --- | --- | --- | --- |
| 48.22)    | on the | ELI5 | dataset. | FRONT       | not   | only     | 5.2 AblationStudy |     |     |     |     |     |     |
| bridges   | these  | gaps | but also | establishes |       | signifi- |                   |     |     |     |     |     |     |
Weconductablationstudiestoverifytheeffective-
| cant leads      | across | all | datasets. | Specifically,   |     | us-  |                                     |     |     |     |     |     |        |
| --------------- | ------ | --- | --------- | --------------- | --- | ---- | ----------------------------------- | --- | --- | --- | --- | --- | ------ |
|                 |        |     |           |                 |     |      | nessofdifferentcomponentsproposedin |     |     |     |     |     | FRONT. |
| ing LLaMA-2-7B, |        |     | FRONT     | comprehensively |     | out- |                                     |     |     |     |     |     |        |
performsChatGPT,achievingincreasesof3.32%,
|         |     |        |             |         |     |        | EffectsofDataGenerationPipeline. |          |        |     |          |        | Asillus- |
| ------- | --- | ------ | ----------- | ------- | --- | ------ | -------------------------------- | -------- | ------ | --- | -------- | ------ | -------- |
| 18.04%, | and | 21.28% | in citation | quality |     | on the |                                  |          |        |     |          |        |          |
|         |     |        |             |         |     |        | trated                           | in §5.1, | simply | SFT | achieves | strong | per-     |
ASQA,ELI5,andQAMPARIdatasetsrespectively.
|     |     |     |     |     |     |     | formance, | underscoring |     | the | high | quality | of our |
| --- | --- | --- | --- | --- | --- | --- | --------- | ------------ | --- | --- | ---- | ------- | ------ |
Thisperformanceunderscorestheeffectivenessof
|     |     |     |     |     |     |     | training | data. | Furthermore, |     | data | filtering, | a cru- |
| --- | --- | --- | --- | --- | --- | --- | -------- | ----- | ------------ | --- | ---- | ---------- | ------ |
FRONTinenhancingattributioncapabilities.
|                                        |     |     |     |     |     |     | cial component |         | of our  | data     | generation |         | pipeline, |
| -------------------------------------- | --- | --- | --- | --- | --- | --- | -------------- | ------- | ------- | -------- | ---------- | ------- | --------- |
|                                        |     |     |     |     |     |     | plays a        | pivotal | role in | ensuring | the        | quality | of the    |
| FRONTexhibitsscalabilitywithmodelsize. |     |     |     |     |     | As  |                |         |         |          |            |         |           |
illustratedatthebottomofTable1,theperformance generated data by filtering out queries that yield
of FRONT in terms of citation quality shows no- non-informativeanswersorfailtomeetattribution
|     |     |     |     |     |     |     | criteria. | To validate |     | the effectiveness |     | of  | our data |
| --- | --- | --- | --- | --- | --- | --- | --------- | ----------- | --- | ----------------- | --- | --- | -------- |
tableimprovementswhenscalingfrom7Bto13B.
filteringstrategies,weconductedexperimentscom-
Specifically,weobserveimprovementsof3.23%in
ASQA,4.97%inELI5,and1.33%inQAMPARI. paringmodelsfine-tunedonbothpre-filteredand
This upward trend underscores the scalability of post-filtereddata. Theresults,depictedinFigure
4,confirmthatmodelstrainedonfiltereddataex-
FRONT withincreasingmodelsize,demonstrating
thepotentialofFRONTinleveragingtheincreased hibitanotableimprovementincitationqualityover
capabilitiesoflargerLLMsforfurtherperformance thosetrainedonunfiltereddata,achievingsuperior
attributionperformancewithreduceddatavolume.
gains.

|     |     |     |     | ASQA |     |     |     | ELI5 |     |     | QAMPARI |     |     |
| --- | --- | --- | --- | ---- | --- | --- | --- | ---- | --- | --- | ------- | --- | --- |
Model
|     |     |     | Correctness |     | Citation |     | Correctness |     | Citation | Correctness |     | Citation |     |
| --- | --- | --- | ----------- | --- | -------- | --- | ----------- | --- | -------- | ----------- | --- | -------- | --- |
EMRec. Rec. Prec. F1. Claim Rec. Prec. F1 Rec.-5 Prec. Rec. Prec. F1
FRONT-7B 40.84 77.70 69.89 73.59 9.18 58.60 55.33 56.92 11.50 21.38 24.74 24.84 24.79
SELF-GUIDE(w/oConsistency) 38.99 70.69 64.48 67.44 10.04 47.63 44.80 46.17 12.18 20.03 22.50 22.58 22.54
VANILLA-SFT(w/oGround) 40.32 67.67 63.67 65.61 9.63 42.30 40.06 41.15 12.86 21.09 21.35 21.36 21.35
FRONT-13B 41.51 78.44 73.66 75.97 9.32 60.31 59.21 59.75 11.94 22.61 24.86 25.39 25.12
SELF-GUIDE(w/oConsistency) 40.99 73.08 68.13 70.52 10.06 50.68 49.78 50.23 13.94 22.38 23.73 23.99 23.85
VANILLA-SFT(w/oGround) 40.85 71.49 66.21 68.75 10.27 46.75 44.47 45.58 12.68 22.80 23.64 23.71 23.67
Table2: AblationstudyontheimpactofdifferenttrainingstageswithintheALCEbenchmark.
44.42
Citation-F1
|                |     |       |       | 46.17 |             |     |     |                     | 33              |     | FRONT-13B |              |     |
| -------------- | --- | ----- | ----- | ----- | ----------- | --- | --- | ------------------- | --------------- | --- | --------- | ------------ | --- |
|                |     |       |       |       |             |     |     |                     |                 |     | F         | -7 B         |     |
|                |     | 43.71 |       |       | SELF-GUIDE  |     |     |                     |                 |     | R O       | N T          |     |
| Citation-Prec. |     |       |       |       |             |     |     |                     | LLaMA2-70B-Chat |     | SEL F- G  | U I D E -13B |     |
|                |     |       | 44.80 |       | PROMPTGUIDE |     |     | ssenlufhtiaF 22..55 |                 |     |           |              |     |
SELF-GUIDE-7B
45.16
| Citation-Rec. |     |       |     |       |       |     |     |     |     |            | Mixtral-8x7B-Inst |     |     |
| ------------- | --- | ----- | --- | ----- | ----- | --- | --- | --- | --- | ---------- | ----------------- | --- | --- |
|               |     |       |     |       | 47.63 |     |     |     | 22  | Vicuna-13B |                   |     |     |
|               |     | 44.00 |     | 46.00 | 48.00 |     |     |     |     |            |                   |     |     |
LLaMA2-7B-Chat
| Figure5: | Ablationstudyofdifferentgroundingguid- |     |     |     |     |     |     | 11..55 |     |     |     |     |     |
| -------- | -------------------------------------- | --- | --- | --- | --- | --- | --- | ------ | --- | --- | --- | --- | --- |
ancestrategiesontheELI5dataset.
|     |     |     |     |     |     |     |     |     | 2200 | 3300 | 4400 5500 | 6600 |     |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | ---- | ---- | --------- | ---- | --- |
CitationF1
EffectsofGroundingGuidedGeneration(G3).
G3
empowers LLMs to first select relevant fine- Figure6: TherelationshipbetweencitationF1andhal-
grainedquotes,whichsubsequentlyguidethegen- lucination: Models positioned closer to the top-right
eration process. These quotes can provide fine- cornerexhibithighercitationqualityandalowerdegree
ofhallucination.
grainedsupervisionsignalsforattributedtextgen-
G3,
| eration.                                | To evaluate |     | the effectiveness |           | of         | we    |         |          |             |     |           |     |        |
| --------------------------------------- | ----------- | --- | ----------------- | --------- | ---------- | ----- | ------- | -------- | ----------- | --- | --------- | --- | ------ |
| conduct                                 | an ablation |     | study             | comparing | it against |       |         |          |             |     |           |     |        |
|                                         |             |     |                   |           |            |       | eration | process. | Experiments |     | conducted |     | on the |
| twovariantswithdistincttrainingrecipes. |             |     |                   |           |            | Given |         |          |             |     |           |     |        |
ELI5datasetusingtheLLaMA-2-7Bmodelshow
that FRONTconsistsoftwostages,werefertothe that SELF-GUIDE outperforms PROMPT-GUIDE.
modeltrainedonlyduringthefirststage(without
ResultsdepictedinFigure5indicatethattraining
| consistency-awarealignment)asSELF-GUIDE. |            |     |         |     |             | We  |            |     |               |           |          |                |        |
| ---------------------------------------- | ---------- | --- | ------- | --- | ----------- | --- | ---------- | --- | ------------- | --------- | -------- | -------------- | ------ |
|                                          |            |     |         |     |             |     | models     | to  | self-generate |           | grounded | quotes         | before |
| firstcompare                             | SELF-GUIDE |     | against |     | VANILLA-SFT |     |            |     |               |           |          |                |        |
|                                          |            |     |         |     |             |     | generating |     | attributed    | responses | is       | more effective |        |
(w/oGround),whichistrainedtodirectlygenerate thansimplyincorporatingthesegroundedquotes
responseswithcitations,bypassingthegrounding
intotheprompt.
| step. The | ablation | study, | detailed |     | in Table | 2, re- |     |     |     |     |     |     |     |
| --------- | -------- | ------ | -------- | --- | -------- | ------ | --- | --- | --- | --- | --- | --- | --- |
veals that models incorporating grounding guid- EffectsofConsistency-AwareAlignment(CCA).
ancesignificantlyoutperformtheir VANILLA-SFT The primary goal of CCA is to enhance the con-
counterpartsthatlacksuchgroundingmechanisms. sistency between grounded quotes and attributed
This highlights the crucial role of grounding in answers, thereby alleviating hallucinations and
|     |     |     |     |     |     |     | achievingmorepreciseattribution. |     |     |     |     | Toevaluatethis, |     |
| --- | --- | --- | --- | --- | --- | --- | -------------------------------- | --- | --- | --- | --- | --------------- | --- |
bolsteringattribution.
Moreover, we explore an alternative variant we compare models that underwent only the G3
of grounding guidance. Considering that SELF- stage(SELF-GUIDE)withthosefurtherenhanced
GUIDE leverages the model itself to both select through the CCA stage (FRONT). As illustrated
grounded quotes and generate attributed answers inTable2,FRONTsignificantlyimprovescitation
in an end-to-end paradigm, a natural variant in- qualityoverSELF-GUIDE,demonstratingtheeffec-
volves breaking down this task into two distinct tivenessoftheCCAstageinenhancingattribution.
stages. Inthisvariant,ChatGPTistaskedwithex- Furthermore,toassessCCA’simpactonreduc-
tractinggroundedquotes. Subsequently,aseparate inghallucinations,weutilizeQAFactEval(Fabbri
modelistrainedtoutilizethesegroundedquotes, etal.,2022),awidelyusedmetricforfactualcon-
along with the query and retrieval documents, to sistency, which scores the consistency of model
directly output the response and citations. This responses to given documents on a scale from 0
variant,referredtoasPROMPT-GUIDED,integrates to5,withhigherscoresindicatinggreaterfaithful-
groundedquotesintotheprompttoguidethegen- ness. Specifically,weanalyzetheperformanceof

leading open-source models and two variants of Authenticity Helpfulness
| FRONTand | SELF-GUIDE |     | ontheELI5dataset. |     | As  |         |     |     |     |      |      |
| -------- | ---------- | --- | ----------------- | --- | --- | ------- | --- | --- | --- | ---- | ---- |
|          |            |     |                   |     |     | ChatGPT |     |     |     | 0.93 | 4.08 |
showninFigure6, FRONTproducesmorefaithful FRONT-7BonASQA 0.94 3.86
outputsthanSELF-GUIDE,significantlyreducing FRONT-7BonELI5 0.92 3.96
| hallucinations. |     |     |     |     |     | FRONT-7BonQAMPARI |     |     |     | 0.86 | 3.62 |
| --------------- | --- | --- | --- | --- | --- | ----------------- | --- | --- | --- | ---- | ---- |
Table3: Humanevaluationonthequalityofgrounded
| EffectsofTrainingDataScale. |     |     |     | Weanalyzethe |     |     |     |     |     |     |     |
| --------------------------- | --- | --- | --- | ------------ | --- | --- | --- | --- | --- | --- | --- |
quotes.
| impact | of the       | data scale | on model | performance |         |     |     |     |     |     |     |
| ------ | ------------ | ---------- | -------- | ----------- | ------- | --- | --- | --- | --- | --- | --- |
| across | two training | stages.    | In       | particular, | we ran- |     |     |     |     |     |     |
tors. Theresultsofthehumanevaluationindicate
domlysampled2k,4k,6k,and8kinstancesfrom
thatbothquotesextractedbyChatGPTandthose
| our full | training | data | across two | distinct | training |     |     |     |     |     |     |
| -------- | -------- | ---- | ---------- | -------- | -------- | --- | --- | --- | --- | --- | --- |
stages. These subsets were then utilized to fine- generated by FRONT are of high quality, further
tunevarious7Bmodelvariants,enablingacompar- substantiatingtheeffectivenessofourmethod.
ativeanalysisofperformancebasedondatascale.
| Results | are shown | in  | Figure | 7, which | indicates |     |     |     |     |     |     |
| ------- | --------- | --- | ------ | -------- | --------- | --- | --- | --- | --- | --- | --- |
7 Conclusion
thatincreasingdatasizeshowssignificantenhance-
mentsincitationquality,indicatingapositivecor- Inthiswork,wepresent FRONT,atwo-stagetrain-
relationbetweendatasizeandmodelperformance. ingframeworkdesignedtoequipLLMswithfine-
AsFRONTimplementsanautomatedprocedureca-
|     |     |     |     |     |     | grained | attribution | capabilities. |     | FRONT | enables |
| --- | --- | --- | --- | --- | --- | ------- | ----------- | ------------- | --- | ----- | ------- |
pableofgeneratinghigh-qualityattributeddataand LLMstoinitiallyselectsupportingquotes,which
constructingcontrastivesupervisionfromweakand thenguidethegenerationprocess. Byfurtheren-
strongLLMs,itholdsthepotentialforcontinuous
|     |     |     |     |     |     | hancing | the consistency |     | between |     | the grounding |
| --- | --- | --- | --- | --- | --- | ------- | --------------- | --- | ------- | --- | ------------- |
performanceimprovements. andgenerationprocessviapreferenceoptimization,
thesesupportingquotescanserveasfine-grained
6 HumanEvaluation
|     |     |     |     |     |     | citations. | Through | comprehensive |     |     | experiments, |
| --- | --- | --- | --- | --- | --- | ---------- | ------- | ------------- | --- | --- | ------------ |
Given the significant impact of the quality of FRONThasdemonstrateditsabilitytogeneratesu-
|          |        |     |              |              |     | perior grounded |                          | responses |     | and highly | supportive |
| -------- | ------ | --- | ------------ | ------------ | --- | --------------- | ------------------------ | --------- | --- | ---------- | ---------- |
| grounded | quotes | on  | fine-grained | verification | for |                 |                          |           |     |            |            |
|          |        |     |              |              |     | citations.      | Furtheranalysisshowsthat |           |     |            | FRONT sig- |
users,weconductedahumanevaluationtoassess
thequalityofgroundedquotesatdifferentstagesof nificantlyreduceshallucinationsandbenefitsuser
verification.
| ourframework:                      |           | (1)QuotesextractedbyChatGPT |             |     |              |              |     |     |     |     |     |
| ---------------------------------- | --------- | --------------------------- | ----------- | --- | ------------ | ------------ | --- | --- | --- | --- | --- |
| from50sampleddatapointsduringtheG3 |           |                             |             |     | stage.       |              |     |     |     |     |     |
| (2) Quotes                         | generated |                             | by FRONT-7B |     | across three | 8 Limitation |     |     |     |     |     |
datasets,with50datapointssampledfromeach.
Weengagedfourannotators,eachwithrelevant Our study presents several limitations worth not-
expertiseandholdingatleastabachelor’sdegree. ing. Firstly, the validation of our framework is
Thequalityofquoteswasevaluatedontwodimen- predominantly conducted on models of sizes 7B
sions: authenticityandhelpfulness. Authenticity and13B,leavingtheexplorationoflargermodels,
(abinaryscaleof0/1)referstowhetherthequotes suchasLLaMA-270Bduetocomputationalcon-
genuinelyoriginatefromthecorrespondingdocu- straints. Secondly,ourframeworkreliesonaprior
ments(quotesthatarehallucinatedormismatched retrievalprocess,whereinrelevantdocumentsare
withthecorrespondingdocumentIDareconsidered retrieved at one time. The incorporation of adap-
inauthentic). Helpfulness (5-point Likert scale) tiveretrieval,enablingmoredynamicinteractions
referstothedegreetowhichthequotesarebenefi- withLLMs,couldpotentiallyenhanceperformance.
cialinaddressingthequery. TheresultsinTable3 Weleaveitforfutureresearch. Lastly,evaluating
representtheaveragescoresforallquoteswithin the correctness of long-form question answering
eachmodelresponse,withtwoannotatorsevaluat- presents inherent challenges, leading our frame-
ingeachresponsetoensurereliability. work to primarily enhance citation quality, with
Furthermore, to evaluate the consistency of modest advancements in correctness. Therefore,
quotequalityannotations,wecomputedtheinter- we advocate for the development of more robust
annotator agreement using Fleiss’ Kappa coeffi- metricscapableofaccuratelyassessingthecorrect-
cient. The obtained Kappa coefficient of 0.82 in- nessoflong-formQAresponses, pavingtheway
| dicates | a high | level of | agreement | among | annota- | forfuturework. |     |     |     |     |     |
| ------- | ------ | -------- | --------- | ----- | ------- | -------------- | --- | --- | --- | --- | --- |

Acknowledgements
qa-basedfactualconsistencyevaluationforsumma-
|     |     |     |     |     |     |     | rization. | InProceedingsofthe2022Conferenceof |     |     |     |
| --- | --- | --- | --- | --- | --- | --- | --------- | ---------------------------------- | --- | --- | --- |
We appreciate Yuchun Fan for providing valu- theNorthAmericanChapteroftheAssociationfor
|                   |     |           |     |     |         |          | ComputationalLinguistics: |     |     | HumanLanguageTech- |     |
| ----------------- | --- | --------- | --- | --- | ------- | -------- | ------------------------- | --- | --- | ------------------ | --- |
| able suggestions. |     | Xiaocheng |     |     | Feng is | the cor- |                           |     |     |                    |     |
nologies,NAACL2022,Seattle,WA,UnitedStates,
| responding | author | of  | this | work. | We  | thank the |     |     |     |     |     |
| ---------- | ------ | --- | ---- | ----- | --- | --------- | --- | --- | --- | --- | --- |
July10-15,2022,pages2587–2601.Associationfor
| anonymous | reviewers |     | for | their | insightful | com- |     |     |     |     |     |
| --------- | --------- | --- | --- | ----- | ---------- | ---- | --- | --- | --- | --- | --- |
ComputationalLinguistics.
| ments. | This | work | was | supported | by  | the Na- |     |     |     |     |     |
| ------ | ---- | ---- | --- | --------- | --- | ------- | --- | --- | --- | --- | --- |
AngelaFan,YacineJernite,EthanPerez,DavidGrang-
| tional Key     | R&D | Program |          | of China | via       | grant No. |            |          |             |                |       |
| -------------- | --- | ------- | -------- | -------- | --------- | --------- | ---------- | -------- | ----------- | -------------- | ----- |
|                |     |         |          |          |           |           | ier, Jason | Weston,  | and Michael | Auli. 2019.    | ELI5: |
| 2021ZD0112905, |     | the     | National |          | Natural   | Science   |            |          |             |                |       |
|                |     |         |          |          |           |           | long form  | question | answering.  | In Proceedings | of    |
| Foundation     | of  | China   | (NSFC)   | (grant   | 62276078, |           |            |          |             |                |       |
the57thConferenceoftheAssociationforCompu-
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longjiang via grant 2022ZX01A32, the Interna- 28-August2,2019,Volume1: LongPapers,pages
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andtheFundamentalResearchFundsfortheCen-
tralUniversities(GrantNo.HIT.OCEF.2023018). ZhangyinFeng,WeitaoMa,WeijiangYu,LeiHuang,
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| and Data | Mining, | Virtual | Event, | CA, | USA, | August |     |     |     |     |     |     |
| -------- | ------- | ------- | ------ | --- | ---- | ------ | --- | --- | --- | --- | --- | --- |
23-27,2020,pages3505–3506.ACM. TheodoraWorledge,JudyHanwenShen,NicoleMeis-
|     |     |     |     |     |     |     | ter,CalebWinston,andCarlosGuestrin.2023. |     |     |     |     | Uni- |
| --- | --- | --- | --- | --- | --- | --- | ---------------------------------------- | --- | --- | --- | --- | ---- |
fyingcorroborativeandcontributiveattributionsin
| Freda Shi, | Xinyun | Chen, | Kanishka | Misra, |     | Nathan |                      |     |     |                      |     |     |
| ---------- | ------ | ----- | -------- | ------ | --- | ------ | -------------------- | --- | --- | -------------------- | --- | --- |
|            |        |       |          |        |     |        | largelanguagemodels. |     |     | CoRR,abs/2311.12233. |     |     |
Scales,DavidDohan,EdH.Chi,NathanaelSchärli,
| andDennyZhou.2023. |     |     | Largelanguagemodelscan |     |     |     |     |     |     |     |     |     |
| ------------------ | --- | --- | ---------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
beeasilydistractedbyirrelevantcontext. InInterna- Fangyuan Xu, Weijia Shi, and Eunsol Choi. 2023a.
tionalConferenceonMachineLearning,ICML2023, RECOMP:improvingretrieval-augmentedlmswith
23-29 July 2023, Honolulu, Hawaii, USA, volume compression and selective augmentation. CoRR,
abs/2310.04408.
202ofProceedingsofMachineLearningResearch,
pages31210–31227.PMLR.
ShichengXu,LiangPang,HuaweiShen,XueqiCheng,
IvanStelmakh,YiLuan,BhuwanDhingra,andMing- andTat-SengChua.2023b. Search-in-the-chain: To-
Wei Chang. 2022. ASQA: factoid questions meet wards the accurate, credible and traceable content
long-formanswers. InProceedingsofthe2022Con- generation for complex knowledge-intensive tasks.
CoRR,abs/2304.14732.
ferenceonEmpiricalMethodsinNaturalLanguage
Processing,EMNLP2022,AbuDhabi,UnitedArab
Emirates,December7-11,2022,pages8273–8288. Ori Yoran, Tomer Wolfson, Ori Ram, and Jonathan
AssociationforComputationalLinguistics. Berant. 2023. Making retrieval-augmented lan-
|       |            |        |               |          |          |       | guage models    | robust | to  | irrelevant | context. | CoRR, |
| ----- | ---------- | ------ | ------------- | -------- | -------- | ----- | --------------- | ------ | --- | ---------- | -------- | ----- |
| Romal | Thoppilan, | Daniel | De            | Freitas, | Jamie    | Hall, | abs/2310.01558. |        |     |            |          |       |
| Noam  | Shazeer,   | Apoorv | Kulshreshtha, |          | Heng-Tze |       |                 |        |     |            |          |       |
Cheng,AliciaJin,TaylorBos,LeslieBaker,YuDu, Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang,
YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Xiaolei Wang, Yupeng Hou, Yingqian Min, Be-
AminGhafouri,MarceloMenegali,YanpingHuang, ichenZhang,JunjieZhang,ZicanDong,YifanDu,
MaximKrikun,DmitryLepikhin,JamesQin,Dehao Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao
Chen,YuanzhongXu,ZhifengChen,AdamRoberts, Jiang,RuiyangRen,YifanLi,XinyuTang,Zikang
MaartenBosma,YanqiZhou,Chung-ChingChang, Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen.
IgorKrivokon,WillRusch,MarcPickett,KathleenS. 2023. A survey of large language models. CoRR,
| Meier-Hellstern, |     | Meredith | Ringel | Morris, |     | Tulsee | abs/2303.18223. |     |     |     |     |     |
| ---------------- | --- | -------- | ------ | ------- | --- | ------ | --------------- | --- | --- | --- | --- | --- |

Kun Zhu, Xiaocheng Feng, Xiyuan Du, Yuxuan Gu, process, we retained 5,667 and 2,431 queries, re-
WeijiangYu,HaotianWang,QianglongChen,Zheng spectively. Additionally,wecalculatedtheaverage
| Chu, JingchangChen, |     | andBingQin.2024. |     | Anin- |     |     |     |     |     |     |
| ------------------- | --- | ---------------- | --- | ----- | --- | --- | --- | --- | --- | --- |
lengthofanswersandtheaveragenumberofcita-
formationbottleneckperspectiveforeffectivenoise
tionsgeneratedforvarioustypesofquerieswithin
filteringonretrieval-augmentedgeneration.
ourdataset,asshowninTable4.
YutaoZhu,HuayingYuan,ShutingWang,JiongnanLiu,
| Wenhan | Liu, Chenlong | Deng, | Zhicheng | Dou, and |     |     |     |     |     |     |
| ------ | ------------- | ----- | -------- | -------- | --- | --- | --- | --- | --- | --- |
A.2 DetailsofDataFiltering
| Ji-RongWen.2023. |     | Largelanguagemodelsforinfor- |                      |     |     |     |     |     |     |     |
| ---------------- | --- | ---------------------------- | -------------------- | --- | --- | --- | --- | --- | --- | --- |
| mationretrieval: |     | Asurvey.                     | CoRR,abs/2308.07107. |     |     |     |     |     |     |     |
WetrainedourAttributedDiscriminatorusingthe
|     |     |     |     |     | manually | annotated | data | provided | by  | Liu et al. |
| --- | --- | --- | --- | --- | -------- | --------- | ---- | -------- | --- | ---------- |
A DetailsofDataGenerationPipeline
|     |     |     |     |     | (2023), | which is | sampled | from | real generative |     |
| --- | --- | --- | --- | --- | ------- | -------- | ------- | ---- | --------------- | --- |
A.1 DataStatistic searchengines. Eachstatementanditsciteddocu-
menthavebeenmeticulouslyannotatedforattribu-
#Questions 8,098 tion,categorizedintothreetypes: completesupport,
|              |     |     |     |      | partialsupport,andnosupport. |     |     |     | Fortraining,weuti- |     |
| ------------ | --- | --- | --- | ---- | ---------------------------- | --- | --- | --- | ------------------ | --- |
| ➥#LongAnswer |     |     |     | 5667 |                              |     |     |     |                    |     |
lizedadatasetof8,834instances,comprising6,415
| ➥#ShortAnswer |     |     |     | 2431 |     |     |     |     |     |     |
| ------------- | --- | --- | --- | ---- | --- | --- | --- | --- | --- | --- |
instancesofcompletesupport,1,552ofpartialsup-
Avg. WordsperAnswer 50.48 port, and 867 of no support. The discriminator
initializedwithLLaMA-2-7B,wastrainedwitha
| ➥Avg. | WordsperLongAnswer  |     |     | 69.15 |                             |     |     |     |                |     |
| ----- | ------------------- | --- | --- | ----- | --------------------------- | --- | --- | --- | -------------- | --- |
|       |                     |     |     |       | maximumsequencelengthof512. |     |     |     | Wetraineditfor |     |
| ➥Avg. | WordsperShortAnswer |     |     | 6.94  |                             |     |     |     |                |     |
3epochs,withatotalbatchsizeof128,andapeak
Avg. CitationperAnswer 4.40 learning rate of 2e-5, incorporating 3% warmup
steps,followedbyalineardecay.
| ➥Avg. | CitationperLongAnswer  |     |     | 4.68 |        |                   |           |           |            |       |
| ----- | ---------------------- | --- | --- | ---- | ------ | ----------------- | --------- | --------- | ---------- | ----- |
| ➥Avg. |                        |     |     |      | During | the data          | filtering | stage,    | we first   | break |
|       | CitationperShortAnswer |     |     | 3.77 |        |                   |           |           |            |       |
|       |                        |     |     |      | down   | the automatically |           | generated | attributed | an-   |
swersintostatementformandusethetraineddis-
| Table 4: | The statistics | of the | data generated | by our |     |     |     |     |     |     |
| -------- | -------------- | ------ | -------------- | ------ | --- | --- | --- | --- | --- | --- |
criminatortoannotatetheattributionbetweeneach
automaticdatagenerationpipeline.
|     |     |     |     |     | statement | and its | cited | documents. | Specifically, |     |
| --- | --- | --- | --- | --- | --------- | ------- | ----- | ---------- | ------------- | --- |
weassigndifferentattributionscorestoeachstate-
Table4presentsthestatisticsofthedataautomat-
|                                             |     |     |     |     | ment s                              | based on | its attribution |     | relationship | with |
| ------------------------------------------- | --- | --- | --- | --- | ----------------------------------- | -------- | --------------- | --- | ------------ | ---- |
| icallygeneratedbyourdatagenerationpipeline. |     |     |     | In  |                                     |          |                 |     |              |      |
|                                             |     |     |     |     | citeddocumentsd,asshowninEquation7. |          |                 |     |              | Con- |
total,wecollected8,098questionsfromtheNatural
sequently,foreachattributedanswer,wecancal-
Questions(NQ)dataset,ofwhich5,667questions
|     |     |     |     |     | culateitsaverageattributionscore. |     |     |     | Attributedan- |     |
| --- | --- | --- | --- | --- | --------------------------------- | --- | --- | --- | ------------- | --- |
weregatheredfromthosewithlong-formanswers,
swerswithanaverageattributionscorebelow0.8
and2,431questionswerecollectedfromthosewith
|     |     |     |     |     | are filtered | out. | The threshold |     | of 0.8 was | deter- |
| --- | --- | --- | --- | --- | ------------ | ---- | ------------- | --- | ---------- | ------ |
short-formfactoidanswers.
minedthroughpreliminarytestingonthedevelop-
Forquestionsrequiringlong-formanswers,we
|     |     |     |     |     | ment set, | for which | we  | manually | annotated | 100 |
| --- | --- | --- | --- | --- | --------- | --------- | --- | -------- | --------- | --- |
initializedourquerysourcewiththeAQUAMUSE
samplestoensuretheeffectivenessofourfiltering
| dataset (Kulkarni |     | et al., 2020), | which consists | of  |     |     |     |     |     |     |
| ----------------- | --- | -------------- | -------------- | --- | --- | --- | --- | --- | --- | --- |
criteria.
high-qualityqueriesspecificallydesignedforlong-
formresponseswithintheNQdataset,recognized
| as“good”bythemajorityofNQevaluators. |     |     |     | Inthis |     |     |     |     |     |     |
| ------------------------------------ | --- | --- | --- | ------ | --- | --- | --- | --- | --- | --- |

|                                              |     |     |     |     |     | 1, Dis(s,d) |     | = complete | support |     |
| -------------------------------------------- | --- | --- | --- | --- | --- | ----------- | --- | ---------- | ------- | --- |
| way,utilizingarefinedandsuperiorqualityquery |     |     |     |     |     |           |     |            |         |     |
setlaidarobustgroundworkforourtrainingdata r(s)= 0.5,Dis(s,d) = partial support

generation,streamliningthedatafilteringprocess. 0, Dis(s,d) = no support
| Forfactoidqueriesthatnecessitateshort-forman- |     |     |     |     |     |     |     |     |     | (7) |
| --------------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
swers,wedirectlysampledfromtheoriginalNQ
| dataset, leveraging |     | its abundance | and | inherently |     |     |     |     |     |     |
| ------------------- | --- | ------------- | --- | ---------- | --- | --- | --- | --- | --- | --- |
B DetailsofEvaluationMetrics
highquality.
During the data generation process, our initial In addition to evaluating citation quality and cor-
querysetcomprised7,725queriesrequiringlong- rectness,theALCEbenchmarkincludesabroader
formanswersand4,000queriesnecessitatingshort- setofdimensions,suchasfluency,ROUGE-L,and
| form answers. |     | After a two-stage | data | filtering | generationlength. |     |     |     |     |     |
| ------------- | --- | ----------------- | ---- | --------- | ----------------- | --- | --- | --- | --- | --- |

Fluency Weevaluatethefluencyofthegenerated framework(Kwonetal.,2023)forefficientinfer-
responseusingMAUVE(Pillutlaetal.,2021). No- ence. Thehyperparametersaresetasillustratedin
| tably,wecalculatefluencyonlyfortheASQAand |     |          |        |          |     |        | Table9. |     |     |     |     |     |     |
| ----------------------------------------- | --- | -------- | ------ | -------- | --- | ------ | ------- | --- | --- | --- | --- | --- | --- |
| ELI5 datasets,                            |     | omitting | it for | QAMPARI, |     | as the |         |     |     |     |     |     |     |
response in QAMPARI typically consists of lists D.2 RetrievalSettings
| ofshortanswers. |     | ArelativelyhighMAUVEscore |     |     |     |     |     |     |     |     |     |     |     |
| --------------- | --- | ------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
Duringtheevaluation,weadoptthesameretrieval
indicatesthatthegenerationissufficientlyfluent.
|         |                                   |     |     |          |     |        | settings                         | as specified |         | by Gao | et        | al. (2023b). | For     |
| ------- | --------------------------------- | --- | --- | -------- | --- | ------ | -------------------------------- | ------------ | ------- | ------ | --------- | ------------ | ------- |
|         |                                   |     |     |          |     |        | the ASQA                         | and          | QAMPARI |        | datasets, | we           | use the |
| ROUGE-L | Inadditiontoevaluatingthecorrect- |     |     |          |     |        |                                  |              |         |        |           |              |         |
|         |                                   |     |     |          |     |        | denseretrieverGTR(Nietal.,2022). |              |         |        |           | FortheELI5   |         |
| ness of | the model-generated               |     |     | content, | we  | employ |                                  |              |         |        |           |              |         |
dataset,weemploythesparseretrieverBM25.
ROUGE-Ltoassesstheoverallqualityandtextual
coherenceoftheresponses.
E MoredetailaboutAblationStudy
C Prompts
E.1 TheEffectofTrainingDataScale.
C.1 PromptsforPrompting-basedMethods Weexaminehowmodelperformancevarieswith
|     |     |     |     |     |     |     | changesindatascale,asdepictedinFigure7. |     |     |     |     |     | The |
| --- | --- | --- | --- | --- | --- | --- | --------------------------------------- | --- | --- | --- | --- | --- | --- |
FollowingGaoetal.(2023b),weadoptthevanilla
|     |     |     |     |     |     |     | upper part | of  | the figure | illustrates |     | the | impact of |
| --- | --- | --- | --- | --- | --- | --- | ---------- | --- | ---------- | ----------- | --- | --- | --------- |
promptingstrategyforitssimplicityandeffective-
|                                      |        |          |          |          |           |           | the training  | data | scale   | on         | citation   | quality  | during    |
| ------------------------------------ | ------ | -------- | -------- | -------- | --------- | --------- | ------------- | ---- | ------- | ---------- | ---------- | -------- | --------- |
| ness. Specifically,                  |        | the      | prompts  | vary     | according | to        |               |      |         |            |            |          |           |
|                                      |        |          |          |          |           |           | the Grounding |      | Guided  | Generation |            | training | stage,    |
| thetypeofdatawithintheALCEbenchmark. |        |          |          |          |           | For       |               |      |         |            |            |          |           |
|                                      |        |          |          |          |           |           | with datasets |      | ASQA,   | ELI5,      | and        | QAMPARI  | rep-      |
| long-form                            | QA     | datasets | such     | as ASQA  |           | and ELI5, |               |      |         |            |            |          |           |
|                                      |        |          |          |          |           |           | resented      | from | left to | right.     | Similarly, |          | the lower |
| the prompt                           | format | is       | detailed | in Table | 5.        | For the   |               |      |         |            |            |          |           |
partofthefiguredescribestheinfluenceduringthe
| short-form | QA  | dataset | QAMPARI, |     | the | format is |     |     |     |     |     |     |     |
| ---------- | --- | ------- | -------- | --- | --- | --------- | --- | --- | --- | --- | --- | --- | --- |
Consistency-AwareAlignmenttrainingstage.
outlinedinTable6.
E.2 TheGeneralizationAcrossModel
| C.2 Instructionsfor |     |     | FRONT |     |     |     |     |     |     |     |     |     |     |
| ------------------- | --- | --- | ----- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
Architectures.
Duringthetrainingprocess,wefollowtheinstruc-
|     |     |     |     |     |     |     | FRONT | demonstrates |     | exceptional |     | generalization |     |
| --- | --- | --- | --- | --- | --- | --- | ----- | ------------ | --- | ----------- | --- | -------------- | --- |
Alpaca6.
| tion format | of  |     | Specifically, |     | we  | employ |     |     |     |     |     |     |     |
| ----------- | --- | --- | ------------- | --- | --- | ------ | --- | --- | --- | --- | --- | --- | --- |
capabilitiesacrossvariousfoundationalmodelar-
variedinstructionsfordifferentquestiontypes,as
|     |     |     |     |     |     |     | chitectures. | Specifically,transitioningthefounda- |     |     |     |     |     |
| --- | --- | --- | --- | --- | --- | --- | ------------ | ------------------------------------ | --- | --- | --- | --- | --- |
delineatedinTable7forlong-formquestionsand
|     |     |     |     |     |     |     | tional model |     | from LLaMA-2-7B |     |     | to the | stronger |
| --- | --- | --- | --- | --- | --- | --- | ------------ | --- | --------------- | --- | --- | ------ | -------- |
Table8forshort-formquestions.
|     |     |     |     |     |     |     | foundational |             | model, | Mistral-7B,  |     | results | in even  |
| --- | --- | --- | --- | --- | --- | --- | ------------ | ----------- | ------ | ------------ | --- | ------- | -------- |
|     |     |     |     |     |     |     | greater      | performance |        | enhancements |     | as      | shown in |
D ExperimentalDetails
|                       |     |     |       |     |     |     | Figure8.                      | Thisfurtherunderscoresthebroadappli- |     |     |        |     |     |
| --------------------- | --- | --- | ----- | --- | --- | --- | ----------------------------- | ------------------------------------ | --- | --- | ------ | --- | --- |
|                       |     |     |       |     |     |     | cabilityandgeneralizabilityof |                                      |     |     | FRONT. |     |     |
| D.1 TrainingDetailsof |     |     | FRONT |     |     |     |                               |                                      |     |     |        |     |     |
Thetrainingofallmodelsisexecutedon4Nvidia E.3 Theeffectofβ inConsistency-Aware
A100GPUs,eachwith80GBofmemory,leverag- AlignmentTrainingStage
ingtheDeepspeed(Rasleyetal.,2020)andHug-
|     |     |     |     |     |     |     | In the | Consistency-Aware |     |     | Alignment |     | Training |
| --- | --- | --- | --- | --- | --- | --- | ------ | ----------------- | --- | --- | --------- | --- | -------- |
gingFaceAcceleratelibraries(Guggeretal.,2022)
|                                        |     |     |     |     |     |       | Stage,     | the β | parameter | in  | Direct | Preference | Op-    |
| -------------------------------------- | --- | --- | --- | --- | --- | ----- | ---------- | ----- | --------- | --- | ------ | ---------- | ------ |
| toconductmulti-GPUdistributedtraining. |     |     |     |     |     | Given |            |       |           |     |        |            |        |
|                                        |     |     |     |     |     |       | timization | (DPO) | controls  |     | the    | strength   | of the |
thelongnatureoftheinputs,themaximumtoken
Kullback-Leiblerpenalty,typicallysetwithinthe
lengthissetto2,048tokens.
|        |     |           |       |            |     |        | range of | 0.1 to | 0.5. | A higher | β   | value | indicates a |
| ------ | --- | --------- | ----- | ---------- | --- | ------ | -------- | ------ | ---- | -------- | --- | ----- | ----------- |
| During | the | grounding | guide | generation |     | stage, |          |        |      |          |     |       |             |
preferenceforthepolicymodel’strainingprocess
modelsaretrainedfor5epochswithatotalbatch
toremainclosertotheinitiallyreferencedmodel.
| size of                                | 128, a peak | learning |     | rate of | 2e-5 | with 3% |                                     |        |     |     |       |        |          |
| -------------------------------------- | ----------- | -------- | --- | ------- | ---- | ------- | ----------------------------------- | ------ | --- | --- | ----- | ------ | -------- |
|                                        |             |          |     |         |      |         | In extreme                          | cases, | as  | β → | 0, we | ignore | the con- |
| warmupstepsfollowedbyalineardecay.     |             |          |     |         |      | During  |                                     |        |     |     |       |        |          |
|                                        |             |          |     |         |      |         | straintsimposedbythereferencemodel. |        |     |     |       |        | Thisset- |
| thecontrastivealignmentstage,wesettheβ |             |          |     |         |      | to0.1   |                                     |        |     |     |       |        |          |
tingaimstobalancethemodel’sabilitytoadaptto
andcontinuedtrainingfortwoadditionalepochs.
newtrainingsignalswhilemaintainingthestability
| Specifically, | During |     | inference, | we  | use | the vllm |     |     |     |     |     |     |     |
| ------------- | ------ | --- | ---------- | --- | --- | -------- | --- | --- | --- | --- | --- | --- | --- |
ofthelearnedbehaviorsfromthereferencemodel.
|     |     |     |     |     |     |     | Subsequently, |     | we  | trained | five | variants | by ad- |
| --- | --- | --- | --- | --- | --- | --- | ------------- | --- | --- | ------- | ---- | -------- | ------ |
6https://github.com/tatsu-lab/stanford_alpaca/
| tree/main |     |     |     |     |     |     | justingβ | from0.1to0.5onthemodelpreviously |     |     |     |     |     |
| --------- | --- | --- | --- | --- | --- | --- | -------- | -------------------------------- | --- | --- | --- | --- | --- |

Instruction: Write an accurate, engaging, and concise answer for the given question using only
the provided search results (some of which might be irrelevant) and cite them properly. Use an
unbiased and journalistic tone. Always cite for any factual claim. When citing several search
results, use [1][2][3]. Cite at least one document and at most three documents in each sentence.
If multiple documents support the sentence, only cite a minimum sufficient subset of the
documents.
Table5: PromptforLong-formQA.
Instruction: Provide a list of accurate answers for the given question using only the provided
search results (some of which might be irrelevant) and cite them properly. Always cite one and
only one document for each answer. Separate answers by commas. For questions that have more than
| 5 answers, | write | at least 5 answers. |     |     |     |     |
| ---------- | ----- | ------------------- | --- | --- | --- | --- |
Table6: PromptforShort-formQA.
| 72.0 |     |       | 48.0 |       | 23.0 |       |
| ---- | --- | ----- | ---- | ----- | ---- | ----- |
| 68.0 |     |       | 46.0 |       | 22.0 |       |
| 64.0 |     |       | 44.0 |       | 21.0 |       |
| 60.0 |     | REC.  | 42.0 | REC.  | 20.0 | REC.  |
| 56.0 |     | PREC. | 40.0 | PREC. | 19.0 | PREC. |
2,000 4,000 6,000 8,000 2,000 4,000 6,000 8,000 2,000 4,000 6,000 8,000
|      |     | DataSize |      | DataSize |      | DataSize |
| ---- | --- | -------- | ---- | -------- | ---- | -------- |
| 76.0 |     |          | 54.0 |          | 25.0 |          |
| 72.0 |     |          | 52.0 |          | 24.0 |          |
| 68.0 |     |          | 50.0 |          | 23.0 |          |
| 64.0 |     |          | 48.0 |          | 22.0 |          |
|      |     | REC.     |      | REC.     |      | REC.     |
| 60.0 |     |          | 46.0 |          | 21.0 |          |
|      |     | PREC.    |      | PREC.    |      | PREC.    |
2,000 4,000 6,000 8,000 2,000 4,000 6,000 8,000 2,000 4,000 6,000 8,000
|     |     | DataSize |     | DataSize |     | DataSize |
| --- | --- | -------- | --- | -------- | --- | -------- |
Figure7: Ablationstudyonsynthetictrainingdatasize: TheupperpartofthefigurecorrespondstotheGrounding
GuidedGenerationtrainingstage, whilethebottompartrepresentstheWeak-to-StrongContrastiveAlignment
trainingstage. Fromlefttoright,theresultsarepresentedforASQA,ELI5,andQAMPARI,respectively. REC.
indicatesCitationRecallandPREC. denotesCitationPrecision. Thex-axisrepresentsthequantityofautomatically
generateddata. Itisobservedthatasthevolumeofautomaticallygenerateddataincreases,thereisaconsistent
improvementinbothcitationrecallandprecisionacrossthetwotrainingstages.
trainedwithG3 toexploretheimpactofthehyper- adheretotheevaluationframeworkestablishedin
parameter β on attribution quality. We evaluated (Gaoetal.,2023b). Forlong-formQAdatasetslike
thesevariantsontheASQAandELI5datasets,and ASQAandELI5,wealsoreportmetricsrelatedto
theexperimentalresultsareshowninFigure9. Fluency,ROUGE-L,andaverageresponselength.
The experimental results indicate that as β in- Specifically,weuseMAUVE(Pillutlaetal.,2021)
creases, the model’s performance on attribution toevaluatethefluencyofthemodelresponse. For
graduallydecreases. Thisobservationsuggeststhat datasetslikeQAMPARI,whereanswersarecom-
thefirststageofG3 mightintroduceanoticeable posed of concatenated entities, we calculate the
inconsistencybetweengroundingandattribution. averagenumberofpredictedentities.
Withhigherβvalues,themodelstrugglestoescape
theconstraintsofinconsistentattributedanswers,
| leading to | a reduction | in attribution | quality as | β   |     |     |
| ---------- | ----------- | -------------- | ---------- | --- | --- | --- |
increases.
F FullResults
Wepresentthecomprehensiveresultsofourexperi-
| mentsinTables10,11,and12. |     | Beyondtheevalua- |     |     |     |     |
| ------------------------- | --- | ---------------- | --- | --- | --- | --- |
tionmetricsrelatedtoCorrectnessandCitation,we

Below is an instruction that describes a task, paired with an input that provides further
| context. | Write | a response | that | appropriately | completes |     | the request. |     |     |     |     |
| -------- | ----- | ---------- | ---- | ------------- | --------- | --- | ------------ | --- | --- | --- | --- |
### Instruction:
Extract the relevant content from the provided documents and then use the extracted content to
| guide               | answer | generation | and | cite the   | sources properly. |     |     |     |     |     |     |
| ------------------- | ------ | ---------- | --- | ---------- | ----------------- | --- | --- | --- | --- | --- | --- |
| ### Input:Question: |        | {Question} |     | Documents: | {Documents}       |     |     |     |     |     |     |
### Response:
|     |     |     | Table7: | InstructionFormatforFRONTonLong-formQA. |     |     |     |     |     |     |     |
| --- | --- | --- | ------- | --------------------------------------- | --- | --- | --- | --- | --- | --- | --- |
Below is an instruction that describes a task, paired with an input that provides further
| context. | Write | a response | that | appropriately | completes |     | the request. |     |     |     |     |
| -------- | ----- | ---------- | ---- | ------------- | --------- | --- | ------------ | --- | --- | --- | --- |
### Instruction:
Extract the relevant content from the provided documents and then use the extracted content to
provide a list of accurate answers for the given question. Always cite one and only one document
| for each            | answer. | Separate   | answers | by         | commas.     |     |     |     |     |     |     |
| ------------------- | ------- | ---------- | ------- | ---------- | ----------- | --- | --- | --- | --- | --- | --- |
| ### Input:Question: |         | {Question} |         | Documents: | {Documents} |     |     |     |     |     |     |
### Response:
|         |                                     |     | Table8: | InstructionFormatforFRONTonShort-formQA. |     |               |     |     |                  |     |     |
| ------- | ----------------------------------- | --- | ------- | ---------------------------------------- | --- | ------------- | --- | --- | ---------------- | --- | --- |
|         | Hyper-parameters                    |     |         | Value                                    |     |               |     |     |                  |     |     |
|         | Top-p                               |     |         | 0.95                                     |     |               |     |     |                  |     |     |
|         | Temperature                         |     |         | 0.2                                      |     |               |     |     |                  |     |     |
|         | Max-length                          |     |         | 2048                                     |     |               |     |     |                  |     |     |
| Table9: | Hyper-parametersettingsininference. |     |         |                                          |     |               |     |     |                  |     |     |
|         |                                     |     |         |                                          |     |               | 72  |     | 56               |     |     |
|         |                                     |     |         |                                          |     | )%(1FnoitatiC | 71  |     | )%(1FnoitatiC 55 |     |     |
Mistral-7B
|     |     |     | VANILLA-SFT(Mistral-7B) |     |     |     | 70  |     | 54  |     |     |
| --- | --- | --- | ----------------------- | --- | --- | --- | --- | --- | --- | --- | --- |
|     |     |     | FRONT(Mistral-7B)       |     |     |     | 69  |     | 53  |     |     |
60
|                  |     |     |     |     |     |     | 68             |             | 52  |                |             |
| ---------------- | --- | --- | --- | --- | --- | --- | -------------- | ----------- | --- | -------------- | ----------- |
| )%(1FnoitatiC 50 |     |     |     |     |     |     | 67             | FRONT-7B    | 51  |                | FRONT-7B    |
| 40               |     |     |     |     |     |     | 0.1 0.2        | 0.3 0.4 0.5 |     | 0.1 0.2        | 0.3 0.4 0.5 |
| 30               |     |     |     |     |     |     | βonASQAdataset |             |     | βonELI5dataset |             |
20
|     |     |     |     |     |     | Figure                                       | 9: Ablation | on hyperparameter |     | β   | in Weak-to- |
| --- | --- | --- | --- | --- | --- | -------------------------------------------- | ----------- | ----------------- | --- | --- | ----------- |
| 10  |     |     |     |     |     | StrongContrastiveAlignmentstageonASQAandELI5 |             |                   |     |     |             |
0
|     | ASQA |     | ELI5 | QAMPARI |     |     |     |     |     |     |     |
| --- | ---- | --- | ---- | ------- | --- | --- | --- | --- | --- | --- | --- |
Figure8:Ablationstudyonmodelarchitecture:Wesub-
stitutedthefoundationmodelinFRONTwithMistral-7B
andcomparedtheexperimentalresultsofmodelsunder
| the same | foundation | model | using | in-context | learning |     |     |     |     |     |     |
| -------- | ---------- | ----- | ----- | ---------- | -------- | --- | --- | --- | --- | --- | --- |
andthosedirectlysupervisedfine-tunedonourautomat-
| icallygenerateddata. |           | Theexperimentsdemonstratethat |     |         |            |     |     |     |     |     |     |
| -------------------- | --------- | ----------------------------- | --- | ------- | ---------- | --- | --- | --- | --- | --- | --- |
| by replacing         | different | foundation                    |     | models, | our frame- |     |     |     |     |     |     |
workstillmaintainsitsgeneralizability.

|           |           | Fluency | Correct. | Citation   |            |        |
| --------- | --------- | ------- | -------- | ---------- | ---------- | ------ |
| ModelType | ModelSize |         |          |            |            |        |
|           |           | (MAUVE) | (EMRec.) | Rec. Prec. | F1 ROUGE-L | Length |
Prompting-based
| ChatGPT      | -   | 73.41 | 40.37 | 72.81 69.69 | 71.22 37.92 | 39.24 |
| ------------ | --- | ----- | ----- | ----------- | ----------- | ----- |
|              | 7B  | 79.90 | 24.32 | 17.24 17.87 | 17.55 29.38 | 42.29 |
| LLaMA-2      | 13B | 87.08 | 27.99 | 16.45 19.04 | 17.65 31.41 | 39.25 |
|              | 70B | 69.28 | 31.53 | 44.18 44.79 | 44.48 31.53 | 26.86 |
|              | 7B  | 66.78 | 29.93 | 55.99 51.66 | 53.74 32.93 | 26.18 |
| LLaMA-2-Chat | 13B | 66.14 | 34.39 | 37.15 38.17 | 37.65 35.13 | 33.68 |
|              | 70B | 86.60 | 41.24 | 60.19 61.16 | 60.67 37.01 | 47.09 |
|              | 7B  | 86.92 | 38.34 | 48.37 44.63 | 46.42 35.95 | 63.90 |
Vicuna-v1.5
|     | 13B | 66.11 | 35.20 | 51.92 53.40 | 52.65 35.74 | 38.57 |
| --- | --- | ----- | ----- | ----------- | ----------- | ----- |
|     | 7B  | 82.37 | 29.46 | 23.12 25.45 | 24.23 31.67 | 37.17 |
Mistral
|     | 8×7B | 83.30 | 36.30 | 32.72 34.49 | 33.58 35.05 | 38.47 |
| --- | ---- | ----- | ----- | ----------- | ----------- | ----- |
|     | 7B   | 82.86 | 38.57 | 64.90 59.67 | 62.18 36.21 | 45.26 |
Mistral-Instruct
|     | 8×7B | 94.77 | 44.11 | 61.80 63.27 | 62.53 38.54 | 58.83 |
| --- | ---- | ----- | ----- | ----------- | ----------- | ----- |
Post-hocRetrieval
| ChatGPT      | -    | 49.78 | 37.68 | 27.11 27.05 | 27.08 36.64 | 52.61  |
| ------------ | ---- | ----- | ----- | ----------- | ----------- | ------ |
|              | 7B   | 75.56 | 16.55 | 13.88 13.86 | 13.87 26.81 | 37.50  |
| LLaMA-2      | 13B  | 77.91 | 20.51 | 20.95 20.94 | 20.94 29.53 | 31.37  |
|              | 70B  | 75.23 | 27.58 | 28.43 28.43 | 28.43 30.33 | 29.88  |
|              | 7B   | 22.50 | 14.17 | 11.33 11.33 | 11.33 21.17 | 110.04 |
| LLaMA-2-Chat | 13B  | 64.52 | 24.43 | 21.43 21.43 | 21.43 33.91 | 41.12  |
|              | 70B  | 70.63 | 29.68 | 24.51 24.51 | 24.51 34.17 | 45.74  |
|              | 7B   | 63.87 | 19.58 | 16.24 16.24 | 16.24 33.22 | 41.80  |
| Vicuna-v1.5  | 13B  | 73.83 | 24.79 | 24.11 24.11 | 24.11 34.42 | 43.54  |
|              | 7B   | 86.54 | 21.17 | 16.78 16.77 | 16.77 30.90 | 42.43  |
| Mistral      | 8×7B | 80.99 | 36.30 | 38.37 35.27 | 36.75 35.05 | 38.47  |
|              | 7B   | 67.97 | 26.26 | 17.87 17.85 | 17.86 33.71 | 51.56  |
Mistral-Instruct 8×7B 65.51 33.90 24.57 24.48 24.52 36.20 53.83
Training-based
|     | 7B  | 74.33 | 29.96 | 67.82 66.97 | 67.39 35.70 | 29.83 |
| --- | --- | ----- | ----- | ----------- | ----------- | ----- |
Self-RAG
|     | 13B | 71.59 | 31.66 | 71.26 70.35 | 70.80 36.01 | 27.03 |
| --- | --- | ----- | ----- | ----------- | ----------- | ----- |
|     | 7B  | 76.66 | 40.32 | 67.67 63.67 | 65.61 38.32 | 62.00 |
VANILLA-SFT
|     | 13B | 84.36 | 40.85 | 71.49 66.21 | 68.75 38.22 | 58.82 |
| --- | --- | ----- | ----- | ----------- | ----------- | ----- |
|     | 7B  | 81.88 | 40.84 | 77.70 69.89 | 73.59 36.95 | 53.93 |
FRONT
|     | 13B | 76.11    | 41.51            | 78.44 73.66 | 75.95 38.63 | 57.56 |
| --- | --- | -------- | ---------------- | ----------- | ----------- | ----- |
|     |     | Table10: | ASQAfullresults. |             |             |       |

|           |           | Fluency | Correct.     | Citation |         |        |
| --------- | --------- | ------- | ------------ | -------- | ------- | ------ |
| ModelType | ModelSize |         |              |          |         |        |
|           |           | (MAUVE) | (Claim) Rec. | Prec. F1 | ROUGE-L | Length |
Prompting-based
| ChatGPT      | -   | 44.65 | 12.47 49.44 | 47.05 48.22 | 20.64 | 90.2   |
| ------------ | --- | ----- | ----------- | ----------- | ----- | ------ |
|              | 7B  | 63.72 | 4.53 3.92   | 5.38 4.54   | 18.27 | 103.36 |
| LLaMA-2      | 13B | 62.19 | 7.77 8.49   | 8.43 8.46   | 19.95 | 88.23  |
|              | 70B | 53.39 | 10.43 23.75 | 22.43 23.07 | 20.43 | 93.84  |
|              | 7B  | 32.80 | 12.47 19.90 | 15.48 17.41 | 20.88 | 96.42  |
| LLaMA-2-Chat | 13B | 29.08 | 13.83 16.50 | 16.09 16.29 | 21.04 | 94.32  |
|              | 70B | 33.69 | 13.30 36.63 | 36.63 36.63 | 21.29 | 117.84 |
|              | 7B  | 31.45 | 12.30 29.81 | 22.45 25.61 | 21.36 | 105.68 |
Vicuna-v1.5
|     | 13B | 37.41 | 14.33 31.15 | 28.99 30.03 | 21.74 | 98.23 |
| --- | --- | ----- | ----------- | ----------- | ----- | ----- |
|     | 7B  | 56.62 | 8.47 16.04  | 16.32 16.18 | 20.46 | 93.80 |
Mistral
|     | 8×7B | 61.83 | 10.43 26.11 | 25.09 25.59 | 20.66 | 93.59 |
| --- | ---- | ----- | ----------- | ----------- | ----- | ----- |
|     | 7B   | 32.74 | 11.07 49.25 | 42.69 45.74 | 20.75 | 98.28 |
Mistral-Instruct
|     | 8×7B | 38.51 | 13.93 49.28 | 48.34 48.81 | 21.34 | 113.71 |
| --- | ---- | ----- | ----------- | ----------- | ----- | ------ |
Post-hocRetrieval
| ChatGPT      | -   | 22.79 | 18.77 14.55 | 14.55 14.55 | 22.28 | 106.83 |
| ------------ | --- | ----- | ----------- | ----------- | ----- | ------ |
|              | 7B  | 72.80 | 7.23 6.84   | 6.84 6.84   | 19.14 | 88.19  |
| LLaMA-2      | 13B | 53.21 | 10.33 9.61  | 9.61 9.61   | 20.63 | 90.44  |
|              | 70B | 58.97 | 11.10 10.27 | 10.26 10.26 | 20.41 | 77.85  |
|              | 7B  | 22.50 | 14.17 11.33 | 11.33 11.33 | 21.17 | 110.04 |
| LLaMA-2-Chat | 13B | 30.36 | 14.93 12.10 | 12.10 12.10 | 21.82 | 109.79 |
|              | 70B | 37.87 | 16.03 12.93 | 12.93 12.93 | 21.57 | 99.94  |
|              | 7B  | 30.88 | 11.83 10.91 | 10.91 10.91 | 21.66 | 99.03  |
Vicuna-v1.5
|     | 13B | 32.59 | 15.20 14.06 | 14.06 14.05 | 14.05 | 108.16 |
| --- | --- | ----- | ----------- | ----------- | ----- | ------ |
|     | 7B  | 52.45 | 10.47 8.64  | 8.64 8.64   | 20.48 | 90.17  |
Mistral
|     | 8×7B | 48.39 | 13.57 11.62 | 11.62 11.62 | 21.43 | 91.97  |
| --- | ---- | ----- | ----------- | ----------- | ----- | ------ |
|     | 7B   | 27.41 | 17.07 13.20 | 13.20 13.20 | 21.52 | 106.93 |
Mistral-Instruct
|     | 8×7B | 27.60 | 17.37 15.68 | 15.68 15.68 | 21.66 | 95.21 |
| --- | ---- | ----- | ----------- | ----------- | ----- | ----- |
Training-based
|     | 7B  | 30.98 | 6.90 22.34 | 32.40 26.45 | 16.48 | 41.66 |
| --- | --- | ----- | ---------- | ----------- | ----- | ----- |
Self-RAG
|     | 13B | 32.04 | 6.07 30.46 | 40.20 34.66 | 15.23 | 38.19 |
| --- | --- | ----- | ---------- | ----------- | ----- | ----- |
|     | 7B  | 44.12 | 9.63 42.30 | 40.06 41.15 | 20.58 | 80.43 |
VANILLA-SFT
|     | 13B | 46.33 | 10.27 46.75 | 44.47 45.58 | 20.56 | 84.01 |
| --- | --- | ----- | ----------- | ----------- | ----- | ----- |
|     | 7B  | 36.90 | 9.18 58.60  | 55.33 56.92 | 19.09 | 74.06 |
FRONT
|     | 13B | 34.37    | 9.32 60.31       | 59.21 59.75 | 19.66 | 75.14 |
| --- | --- | -------- | ---------------- | ----------- | ----- | ----- |
|     |     | Table11: | ELI5fullresults. |             |       |       |

|           |           | Correctness  | Citation   |             |
| --------- | --------- | ------------ | ---------- | ----------- |
| ModelType | ModelSize |              |            |             |
|           |           | Rec.-5 Prec. | Rec. Prec. | F1 NumPred. |
Prompting-based
| ChatGPT      | -   | 20.28 19.84 | 19.06 22.03 | 20.44 4.71 |
| ------------ | --- | ----------- | ----------- | ---------- |
|              | 7B  | 12.56 11.32 | 6.03 6.35   | 6.19 7.02  |
| LLaMA-2      | 13B | 18.00 12.39 | 5.45 5.74   | 5.59 11.31 |
|              | 70B | 18.50 14.79 | 10.10 10.50 | 10.30 8.31 |
|              | 7B  | 17.96 19.74 | 9.58 9.68   | 9.63 4.73  |
| LLaMA-2-Chat | 13B | 21.34 18.86 | 8.94 9.06   | 9.00 6.51  |
|              | 70B | 22.62 18.04 | 13.49 13.98 | 13.73 7.44 |
|              | 7B  | 14.22 14.74 | 11.26 11.64 | 11.45 5.87 |
Vicuna-v1.5
|     | 13B | 22.06 19.60 | 13.04 13.74 | 13.38 7.62 |
| --- | --- | ----------- | ----------- | ---------- |
|     | 7B  | 16.96 15.98 | 7.50 7.76   | 7.63 6.29  |
Mistral
|     | 8×7B | 18.18 15.63 | 9.72 10.20  | 9.95 6.63  |
| --- | ---- | ----------- | ----------- | ---------- |
|     | 7B   | 17.52 21.29 | 17.56 18.53 | 18.03 4.54 |
Mistral-Instruct
|     | 8×7B | 20.12 19.64 | 19.27 20.38 | 19.81 5.32 |
| --- | ---- | ----------- | ----------- | ---------- |
Post-hocRetrieval
| ChatGPT      | -   | 25.14 22.85 | 12.29 12.29 | 12.29 5.46 |
| ------------ | --- | ----------- | ----------- | ---------- |
|              | 7B  | 6.48 5.11   | 5.05 5.05   | 5.05 6.55  |
| LLaMA-2      | 13B | 9.88 7.17   | 5.20 5.20   | 5.20 6.98  |
|              | 70B | 14.44 12.44 | 7.49 7.49   | 7.49 7.41  |
|              | 7B  | 12.94 10.89 | 7.76 7.76   | 7.76 5.99  |
| LLaMA-2-Chat | 13B | 15.72 12.23 | 7.87 7.87   | 7.87 6.32  |
|              | 70B | 17.90 14.45 | 9.05 9.05   | 9.05 6.05  |
|              | 7B  | 12.04 9.71  | 6.69 6.69   | 6.69 7.10  |
Vicuna-v1.5
|     | 13B | 14.78 11.47 | 8.50 8.50 | 8.50 6.67 |
| --- | --- | ----------- | --------- | --------- |
|     | 7B  | 9.94 7.90   | 6.00 6.00 | 6.00 7.38 |
Mistral
|     | 8×7B | 13.92 12.08 | 6.70 6.70 | 6.70 6.58 |
| --- | ---- | ----------- | --------- | --------- |
|     | 7B   | 15.80 12.15 | 8.34 8.34 | 8.34 7.01 |
Mistral-Instruct
|     | 8×7B | 24.16 18.28 | 9.78 9.78 | 9.78 7.37 |
| --- | ---- | ----------- | --------- | --------- |
Training-based
|     | 7B  | 2.34 1.98 | 10.53 18.80 | 13.50 3.49 |
| --- | --- | --------- | ----------- | ---------- |
Self-RAG
|     | 13B | 1.90 1.33   | 12.79 20.90 | 15.86 3.08 |
| --- | --- | ----------- | ----------- | ---------- |
|     | 7B  | 12.86 21.09 | 21.35 21.36 | 21.35 7.49 |
VANILLA-SFT
|     | 13B | 12.68 22.80 | 23.64 23.71 | 23.67 3.14 |
| --- | --- | ----------- | ----------- | ---------- |
|     | 7B  | 11.50 21.38 | 24.74 24.84 | 24.79 3.08 |
FRONT
|     | 13B      | 11.94 22.61         | 24.86 25.39 | 25.12 3.17 |
| --- | -------- | ------------------- | ----------- | ---------- |
|     | Table12: | QAMPARIfullresults. |             |            |