q129/random_k4/random_4.md

| MMBench: |     |     | Is Your |     | Multi-modal |     | Model |     | an All-around |
| -------- | --- | --- | ------- | --- | ----------- | --- | ----- | --- | ------------- |
Player?
YuanLiu1,∗,HaodongDuan1,∗,‡,YuanhanZhang2,∗,BoLi2,∗,SongyangZhang1,∗,
WangboZhao4,YikeYuan5,JiaqiWang1,ConghuiHe1,ZiweiLiu2,†,KaiChen1,†
4202 guA 02  ]VC.sc[  5v18260.7032:viXra DahuaLin1,3,†
|     |                                 | 1ShanghaiAILaboratory |     |     | 2NanyangTechnologicalUniversity |                                |     |     |     |
| --- | ------------------------------- | --------------------- | --- | --- | ------------------------------- | ------------------------------ | --- | --- | --- |
|     | 3TheChineseUniversityofHongKong |                       |     |     |                                 | 4NationalUniversityofSingapore |     |     |     |
5ZhejiangUniversity
|     |     | ∗EqualContribution |     |     | ‡ProjectLead |     | †CorrespondingAuthor |     |     |
| --- | --- | ------------------ | --- | --- | ------------ | --- | -------------------- | --- | --- |
Abstract
Largevision-languagemodels(VLMs)haverecentlyachievedremarkableprogress,
|     | exhibiting                     |     | impressive | multimodal | perception                           |         | and reasoning |            | abilities. However, |
| --- | ------------------------------ | --- | ---------- | ---------- | ------------------------------------ | ------- | ------------- | ---------- | ------------------- |
|     | effectively                    |     | evaluating | these      | large VLMs                           | remains | a major       | challenge, | hindering           |
|     | futuredevelopmentinthisdomain. |     |            |            | TraditionalbenchmarkslikeVQAv2orCOCO |         |               |            |                     |
Captionprovidequantitativeperformancemeasurementsbutlackfine-grainedabil-
|     | ityassessmentandrobustevaluationmetrics. |     |     |     |     |     | Meanwhile,subjectivebenchmarks, |     |     |
| --- | ---------------------------------------- | --- | --- | --- | --- | --- | ------------------------------- | --- | --- |
suchasOwlEval,offercomprehensiveevaluationsofamodel’sabilitiesbyincor-
poratinghumanlabor,whichisnotscalableandmaydisplaysignificantbias. In
responsetothesechallenges,weproposeMMBench,abilingualbenchmarkfor
|     | assessingthemulti-modalcapabilitiesofVLMs. |     |                                                             |     |           |           | MMBenchmethodicallydevelops |     |     |
| --- | ------------------------------------------ | --- | ----------------------------------------------------------- | --- | --------- | --------- | --------------------------- | --- | --- |
|     | acomprehensiveevaluation                   |     |                                                             |     | pipeline, | primarily | comprisedofthefollowingkey  |     |     |
|     | features:                                  |     | 1. MMBenchismeticulouslycuratedwithwell-designedqualitycon- |     |           |           |                             |     |     |
trolschemes,surpassingexistingsimilarbenchmarksintermsofthenumberand
|     | varietyofevaluationquestionsandabilities;2. |     |     |     |     |     | MMBenchintroducesarigorous |     |     |
| --- | ------------------------------------------- | --- | --- | --- | --- | --- | -------------------------- | --- | --- |
CircularEvalstrategyandincorporateslargelanguagemodelstoconvertfree-form
predictionsintopre-definedchoices,whichhelpstoyieldaccurateevaluationre-
|     | sults | for | models with | limited | instruction-following |     | capabilities. |     | 3. MMBench |
| --- | ----- | --- | ----------- | ------- | --------------------- | --- | ------------- | --- | ---------- |
incorporatesmultiple-choicequestionsinbothEnglishandChineseversions,en-
ablinganapples-to-applescomparisonofVLMs’performanceunderabilingual
|     | context.                                                    |     | Tosummarize,MMBenchisasystematicallydesignedobjectivebench- |     |     |     |     |     |        |
| --- | ----------------------------------------------------------- | --- | ----------------------------------------------------------- | --- | --- | --- | --- | --- | ------ |
|     | markforarobustandholisticevaluationofvision-languagemodels. |     |                                                             |     |     |     |     |     | Wehope |
MMBenchwillassisttheresearchcommunityinbetterevaluatingtheirmodelsand
|     | facilitatefutureprogressinthisarea. |     |     |     | TheevalutationcodeofMMBenchhasbeen |     |     |     |     |
| --- | ----------------------------------- | --- | --- | --- | ---------------------------------- | --- | --- | --- | --- |
|     | integratedintoVLMEvalKit[14].       |     |     |     | 1                                  |     |     |     |     |
1 Introduction
Recently,notableprogresshasbeenachievedwithintherealmoflargelanguagemodels(LLMs).
For instance, the latest LLMs, such as OpenAI’s ChatGPT and GPT-4 [37], have demonstrated
remarkablereasoningcapabilitiesthatarecomparableto,andinsomecases,evensurpasshuman
capabilities. DrawinginspirationfromthesepromisingadvancementsinLLMs,largevision-language
models (LVLMs) have also experienced a revolutionary transformation. Notable works, such as
1ThisisarevisedversionreleasedinApril2024. ItdescribesMMBenchv1.1, arefinedversionofthe
MMBench(withbetterdataquality).Pleaserefertohttps://arxiv.org/pdf/2307.06281v3forthe
previousversion,whichisreleasedinAugust2023.
Technicalreport

Future
Identity Prediction Function
Reasoning Reasoning
Celebrity
Image Recognition
Emotion
Image Attribute
Quality Recognition GPT4-V
Image Attribute Gemini-Pro-V
Scene Comparison
Qwen-VL-Max
Image Action InternLM-XComposer2
Style Recognition
LLaVA-v1.5-13B
Image Structuralized CogVLM-Chat-17B
Topic Image-Text
Understanding Yi-VL-34B
Natural MiniCPM-V
Relation Spatial
Relationship
Object Social
Localization Relation
OCR Physical R P e h l y a s t i i c o a n l
Property
Figure1: Resultsofeightrepresentativelargevision-languagemodels(VLMs)acrossthe20
abilitydimensionsdefinedinMMBench-test.
GPT-4v [37], Gemini-Pro-V [44] and LLaVA [33], have demonstrated enhanced capabilities in
imagecontentrecognitionandreasoningwithinthedomainofvision-languagemodels,exhibiting
superior performance compared to earlier works. Nevertheless, a large proportion of the early
studies [18, 56, 33] tend to emphasize showcasing qualitative examples rather than undertaking
comprehensiveandquantitativeexperimentstothoroughlyassesstheirmodelperformance. Thelack
ofquantitativeassessmentposesaconsiderablechallengeforcomparingvariousmodels. Recent
studieshaveprimarilyexploredtwoapproachestoconductquantitativeevaluations.Thefirstapproach
involvesutilizingexistingpublicdatasets[19,9]forobjectiveevaluation,whilethesecondapproach
employshumanannotators[49,48]toperformsubjectiveevaluations. However,itisworthnoting
thatbothapproachesexhibitsomeinherentlimitations.
A multitude of public datasets, such as VQAv2 [19], COCO Caption [9], GQA [23], and OK-
VQA[35],havelongservedasvaluableresourcesforthequantitativeevaluationofVLMs. These
datasetsofferobjectivemetrics,includingaccuracy,BLEU,CIDEr,etc. However,whenemployedto
evaluatemoreadvancedLVLMs,thesebenchmarksencounterthefollowingchallenges. 1. False
NegativeIssues: Mostexistingevaluationmetricsrequireanexactmatchbetweentheprediction
and the reference target, leading to potential limitations. For instance, in the VQA task, even if
thepredictionis“bicycle”whilethereferenceansweris“bike”,theexistingmetricwouldassign
a negative score to the prediction, resulting in a considerable number of false-negative samples.
2. LackingFinegrainedAnalysis: Currentpublicdatasetspredominantlyfocusonevaluatinga
model’sperformanceonspecifictasks,offeringlimitedinsightsintothefine-grainedcapabilities
ofthesemodels. Thus,theyprovideinsufficientfeedbackregardingpotentialdirectionsforfuture
improvements.
Giventheaforementionedchallenges,recentstudies,suchasOwlEval[49]andLVLM-eHub[48]
proposehuman-involvedsubjectiveevaluationstrategies,aimingtoaddressexistingmethods’limita-
tionsbyincorporatinghumanjudgmentandperceptionintheevaluationprocess. OwlEvalartificially
constructs82open-endedquestionsbasedonimagesfrompublicdatasetsandemployshumananno-
tatorstoassessthequalityofVLMpredictions. Similarly,inspiredbyFastChat[53],LVLM-eHub
developsanonlineplatformwheretwomodelsarepromptedtoanswerthesamequestionrelatedto
animage. Aparticipantthencomparestheanswersprovidedbytwomodels. Subjectiveevaluation
strategiesoffernumerousbenefits. Theseincludeaccuratematching,wherehumanscanprecisely
correlateapredictionwiththetarget,evenwhenexpressedindifferentwords,andcomprehensive
assessment,wherehumansareinclinedtojuxtaposetwopredictionsconsideringmultiplefacets. The
ultimatescoreiscomputedasthemeanscoreacrossdiverseabilities,facilitatingaholisticevaluation
ofthemodel’scapabilities.
WhilesubjectiveevaluationallowsforamorecomprehensiveassessmentofaVLM,italsointroduces
newchallenges. Firstly,humanevaluationsareinherentlybiased. Consequently,itbecomeschalleng-
ingtoreproducetheresultspresentedinaworkwithadifferentgroupofannotators. Also,existing
subjectiveevaluationstrategiesfacescalabilityissues. Employingannotatorsformodelevaluation
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aftereachexperimentisanexpensiveendeavor. Moreover,evaluationdatasetsofsmallsizescan
resultinstatisticalinstability. Toensurearobustevaluation,collectingmoredatabecomesnecessary,
whichinturndemandsasignificantamountofhumanlabor.
Inlightofthechallengesfacedbyconventionalobjectiveandsubjectivebenchmarks,wepropose
MMBench,asystematicallydesignedobjectiveevaluationbenchmarktorobustlyevaluatedifferent
abilitiesoflargevision-languagemodels. Currently,MMBenchcontainsover3000multiple-choice
questionscovering20differentabilitydimensions,suchasobjectlocalizationandsocialreasoning,
forevaluatingvision-languagemodels. Eachabilitydimensionencompassesover125questions,
with the quantity of questions per ability maintained at a roughly equal level. The distribution
facilitatesabalancedandthoroughassessmentoftheseabilities. SincesomeexistingVLMshave
limitedinstruction-followingcapabilityandcannotdirectlyoutputchoicelabels(A,B,C,etc.) for
MMBenchquestions,theevaluationbasedonexactmatchingmaynotyieldaccurateandreasonable
conclusions. Inordertoreducethenumberoffalse-negativesamplesduringanswermatching,we
employGPT-4tomatchamodel’spredictiontocandidateschoicesinamulti-choicequestionand
then output the label for the matched choice. We conduct a comparison between GPT-4-based
choicematchingandhumanevaluations,anddiscoveredthatGPT-4canaccuratelymatchhuman
assessmentsin91.5%ofcases,demonstratingitsgoodalignmentandrobustnessasachoiceextractor.
Tomaketheevaluationmorerobust,weproposeanovelevaluationstrategy,namedCircularEval
(detailsinSec.4.3). Wecomprehensivelyevaluate21well-knownvision-languagemodels(across
differentmodelarchitecturesandscales)onMMBenchandreporttheirperformanceondifferent
abilitydimensions. Theperformancerankingoffersadirectcomparisonbetweenvariousmodels
andprovidesvaluablefeedbackforfutureoptimization. Insummary, ourmaincontributionsare
three-fold:
• Systematically-constructedDataset: TothoroughlyevaluatethecapacityofaVLM,wecarefully
curatedadatasetcomprisingatotalof3,217meticulouslyselectedquestions, coveringadiverse
spectrumof20fine-grainedskills.
• RobustEvaluation: Weintroduceanovelcircularevaluationstrategy(CircularEval)toimprove
the robustness of our evaluation process. After that, GPT-4 is employed to match the model’s
predictionwithgivenchoices, whichcansuccessfullyextractchoicesevenfrompredictionsofa
VLMwithpoorinstruction-followingcapability.
• AnalysisandObservations: Weperformacomprehensiveevaluationofaseriesofwell-known
vision-language models using MMBench, and the evaluation results can provide insights to the
researchcommunityforfutureimprovement.
2 RelatedWork
2.1 MultimodalDatasets
Large-scale VLMs have shown promising potential in multimodal tasks such as complex scene
understanding and visual question answering. Though qualitative results so far are encouraging,
quantitative evaluation is of great necessity to systematically evaluate and compare the abilities
ofdifferentVLMs. Recentworkshaveevaluatedtheirmodelsonnumerousexistingpublicmulti-
modalitydatasets. COCOCaption[9],Nocaps[3],andFlickr30k[51]providehuman-generated
image captions and the corresponding task is to describe the image content in the form of text.
Visualquestionansweringdatasets,suchasGQA[23],OK-VQA[35],VQAv2[19],andVizwiz[20],
containquestion-answerpairsrelatedtothegivenimage,usedtomeasurethemodel’sabilityonvisual
perceptionandreasoning. Somedatasetsprovidemorechallengingquestion-answeringscenariosby
incorporatingadditionaltasks. Forexample,TextVQA[42]proposesquestionsabouttextshownin
theimage,thusinvolvingtheOCRtaskinquestion-answering. ScienceQA[34]focusesonscientific
topics,requiringthemodeltointegratecommonsenseintoreasoning. Youcook2[55]replacesimages
withvideoclips,introducingadditionaltemporalinformation. However,theaforementioneddatasets
aredesignedonspecificdomains,andcanonlyevaluatethemodel’sperformanceononeorseveral
tasks. Besides,differentdataformatsandevaluationmetricsacrossdatasetsmakeitmoredifficultto
comprehensivelyassessamodel’scapability. Yeetal.[49]constructedOwlEval,anevaluationset
encompassingavarietyofvisual-relatedtasks,albeitofalimitedsize. Fuetal.[17]introducedMME,
whichassessesaVLM’scapabilitiesfromvariousperspectivesatasmallscale. Divergingfromprior
3

works,inthispaper,wepresentanovelmultimodalbenchmark,MMBench. Wealsodeviseasuiteof
evaluationstandardsaimedatensuringthestabilityandaccuracyoftheevaluationresults.
2.2 MultimodalModels
BuildinguponthesuccessofLargeLanguageModels(LLMs)suchasGPTs[41,7,40],LLaMA[46],
andVicuna[53],recentadvancementshavebeenmadeinmultimodalmodels. Flamingo[4],anearly
attemptatintegratingLLMsintovision-languagepretraining,hasmadesignificantstrides. Tocondi-
tioneffectivelyonvisualfeatures,itincorporatesseveralgatedcross-attentiondenseblockswithin
pretrainedlanguageencoderlayers. OpenFlamingo[4]offersanopen-sourceversionofthismodel.
BLIP-2 [28] introduces a Querying Transformer (Q-former) to bridge the modality gap between
thefrozenimageencoderandthelargelanguageencoder. Subsequently,InstructBLIP[11]extends
BLIP-2[28]withvision-languageinstructiontuning,achievingsuperiorperformance. MiniGPT-
4 [56] attributes the prowess of GPT-4 [37] to advanced LLMs and proposes the use of a single
projectionlayertoalignthevisualrepresentationwiththelanguagemodel. LLaVA[33]alsoutilizes
GPT-4togenerateinstruction-followingdataforvision-languagetuning. Thelearningparadigmand
themultimodalinstructiontuningcorpusproposedbyLLaVAarewidelyadoptedbysubsequent
works[32,8,2,10]. Duringtheinstructiontuning,Low-RankAdaptation(LoRA[22])hasbeen
adopted by recent works [49, 12, 10] on language models to achieve better performance on mul-
timodalunderstanding. Intherealmofproprietarymodels,theAPIsofmultiplepowerfulVLMs
havealsobeenmadepubliclyavailabletoprosperdownstreamapplications,includingGPT-4v[37],
Gemini-Pro-V[44],andQwen-VL-Max[6]. Afterconductingathoroughevaluationofthesemodels
ontheproposedMMBench,weofferinsightsforfuturemultimodalresearch.
3 TheconstructionofMMBench
ThreecharacteristicsdifferentiateMMBenchfromexistingbenchmarksformulti-modalityunder-
standing: i)MMBenchadoptsimages/problemsfromvarioussourcestoevaluatediversifiedabilities
inahierarchicaltaxonomy;ii)MMBenchperformsrigorousqualitycontroltoensurethecorrectness
andvalidityoftestingsamples;iii)MMBenchisabilingualmulti-modalbenchmarkandenablesan
apple-to-applecomparisonofVLMperformanceunderEnglishandChinesecontexts. Belowwewill
delveintomoredetailsoftheconstructionofMMBench.
3.1 TheHierachicalAbilityTaxonomyofMMBench
Humanpossessremarkableperceptionandreasoningcapabilities. Theseabilitieshavebeencrucial
inhumanevolutionandserveasafoundationforcomplexcognitiveprocesses. Perceptionrefersto
gatheringinformationfromsensoryinputs,whilereasoninginvolvesdrawingconclusionsbasedon
thisinformation. Together,theyformthebasisofmosttasksintherealworld,includingrecognizing
objects,solvingproblems,andmakingdecisions[36,16]. Inpursuitofgenuinegeneralartificial
intelligence(AGI),vision-languagemodels(VLMs)arealsoexpectedtoexhibitstrongperception
andreasoningabilities. Therefore,weadoptPerceptionandReasoningaslevel-1(L-1)abilitiesin
ourtaxonomy. Afterthat,weincorporatemorefine-grainedabilitydimensionsintothetaxonomy,
andcategorizethemintosixL-2andtwentyL-3abilitydimensions. Wedisplaytheabilitytaxonomy
inFigure2andyoucanfinddetaileddefinitionsofeachfine-grainedabilityintheAppendix.
3.2 DataCollectionandQualityControl
QuestionCollection. InMMBench, wecollectvision-languageQAsintheformatofmultiple-
choiceproblemsforeachL-3ability. AproblemP correspondstoaquadruple(Q ,C ,I ,A ). Q
i i i i i i
denotesthequestion,C representsasetwithn(2≤n≤4)choicesc ,c ,...,c ,I correspondsto
i 1 2 n i
theimageassociatedwiththequestion,andA isthecorrectanswer. Thedata—includingimages,
i
choices,andquestions—aremanuallycollectedfrommultiplesourcesbyagroupofvolunteers. For
eachL-3ability,wefirstsetanexamplebycompiling10∼20multiple-choicequestions. Thenwe
enlistthevolunteers,allofwhomareundergraduateorgraduatestudentsfromvariousdisciplines,to
expandtheproblemset. Theexpansionisbasedontheabilitydefinitionandpotentialdatasources,
which include both public datasets and the Internet. According to the statistics, more than 80%
of questions in MMBench are collected from the Internet. For the remaining 20% samples, the
4

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[1 T Im 5 op 0 a P [ i 1 ] g c r P o e 3 h p 2 y e ] s r i t c y a [ l 1 R 5 e 0 F la u ] t n io c n tion A [1 t 2 R R 7 t ea ] e r so a i n I b d i s n e [ u n g o 4 ti t n ty 0 e i n 9 g ] [1 2 5 ] P r e d ictio R n F u t R u r e e L e [ a a o 3 s s g 0 o o ic S n t r 8 u ct u n r ali a z e i ] n l I m i a g e - T e xt n g U n d e rst a n d g i n [ 1 8 3] R [4 e 3 a R s 2 S e o o ] l c n i a R al e i t l a n t i i o [ o g 1 n 5 n 3] Phy R s e ic la a [ t 1 l io 2 n 5 N ] R at e u l [ a r 1 t R a A 5 i l o e t 4 n c t ] o [ r 1 g ib n 4 u i 9 t t e i ] on
[1149]
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n
oiti n g o c e R n oit c A [1 2 6 ] C o m p a r iso
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A ttr ib u te [1 2 e 5 la ] tio n S s p h a ip tia l
Figure 2: Ability dimensions in MMBench. Currently, MMBench incorporates three levels of
abilitydimensions,encompassing20distinctleafabilities.
(a) Multi-Modal Questions (b)
Filter TEXT-ONLY Filter WRONG
Text-Only Inference Multi-Modal Inference
with SOTA LLMs with SOTA VLMs
GPT-4 Qwen-Max GPT-4v LLaVA-v1.5
Gemini-Pro Gemini-Pro-Vision …
English Version (Original) ChineseVersion (Translated)
QUESTION. Think about the magnetic force between the QUESTION. 考虑每对磁铁之间的磁力。
If the majority is Correct IfallVLMs are Wrong magnets in each pair. Which of the following statements is true? 以下哪个陈述是正确的？
A. The magnitude of the magnetic force is smaller in Pair 2. A. 第二对磁铁之间的磁力大小较小。
Human Verification B C pa . . i T T r h s h . e e m m a a g g n n i i t t u u d d e e o o f f t t h h e e m m a a g g n n e e t t i i c c f f o o r r c c e e i i s s t sm he a s ll a e m r e in i n P a b i o r t 1 h . B C . . 第 两 一 对 对 磁 磁 铁 铁 之 之 间 间 的 的 磁 磁 力 力 大 大 小 小 相 较 同 小 。 。
Figure3: TheconstructionofMMBench. (a). ThequalitycontrolstrategiesadoptedinMMBench;
(b)AnillustrationofquestionsinMMBench-CN.
imagesaregatheredfromthevalidationsetofpublicdatasets(iftheyexist)whilethequestionsare
self-constructed,whichisnotsupposedtobeusedfortraining. IntheAppendix,welistdatasources
usedincollectionandprovidevisualizationofsamplescorrespondingtoeachL-3ability.
QualityControl. Rawdatacollectedfromvolunteersmayincludewrongorunqualifiedsamples.
Duringinvestigation, wefindthatthereexisttwomajorpatternsforsuchsamples: i)theanswer
tothequestioncanbeinferredwithtext-onlyinputs,whichmakesitinappropriateforevaluating
themultimodalunderstandingcapabilityofVLMs; ii)thesampleissimplywrong,eitherwitha
flawedquestion,choices,oranincorrectanswer. Wedesigntwostrategiestofilterthoselow-quality
samples,whichisvisualizedinFigure3(a). Weadopt‘majorityvoting’todetecttext-onlysamples:
datasamplesareinferredwithstate-of-the-artLLMs(GPT-4[37],Gemini-Pro[44],etc.). Ifmore
thanhalfoftheLLMscananswerthequestioncorrectlywithtext-onlyinputs,thequestionwillbe
manuallyverifiedandthenremovedifitisunqualified. Todetectwrongsamples,wealsoimplement
anautomaticfilteringmechanism. Weselectseveralstate-of-the-artVLMs(includingbothopen-
5

Ground-Truth Answer MiniGPT-4-13B InstructBLIP-13B VisualGLM-6B
Distribution (Rolling) Prediction Distribution Prediction Distribution Prediction Distribution
A：26.4% A：23.2% A：36.0% A：31.5%
B A B：26.4% B A B：42.1% A B：39.6% B A B：28.4%
D C：25.1% D C：19.2% B D C：11.1% D C：19.8%
C C C C
D：22.1% D：15.5% D：13.3% D：20.3%
Figure4: Thechoicedistributionofground-truthanswersandpredictionsofsampleVLMs(all
CircularEvalrecords). Sincethereexistquestionswithonly2/3choicesinMMBench,thechoice
distributionofground-truthisnotexactlyeven.
source and proprietary ones), to answer all questions in MMBench . If all VLMs fail to answer
thequestioncorrectly, weconsiderthisquestionpotentiallyproblematic. Suchquestionswillbe
manuallycheckedandexcludediftheyareactuallywrong. Thequalitycontrolparadigmhelpsusto
constructhigh-qualitydatasetsandcanalsobeusedtocleanotherexistingbenchmarks.
MMBench-CN. We further convert the curated MMBench into a Chinese version. During the
process, all content in questions and choices are translated to Chinese based on GPT-4, except
forpropernouns,symbols,andcode. Allthosetranslationsareverifiedbyhumanstoensurethe
validity. MMBench-CNenablesanapple-to-applecomparisonofVLMperformanceunderEnglish
andChinesecontexts. AnexampleinMMBench-CNisillustratedinFigure3(b).
3.3 MMBenchStatistics
DataStatistics. Inthepresentstudy,wehavegatheredatotalof3,217datasamplesspanningacross
20distinctL-3abilities. Wedepicttheproblemcountsofallthe3levelsofabilitiesinFigure2.
To ensure a balanced and comprehensive evaluation for each ability, we try to maintain an even
distributionamongproblemsassociatedwithdifferentabilitiesduringdatacollection,withatleast
125samplesforeachL-3category.
DataSplits. Wefollowthestandardpracticeinpreviousworks[35]tosplitMMBenchintodev
andtestsubsetsataratioof4:6. Forthedevsubset,wemakealldatasamplespubliclyavailable
alongwiththegroundtruthanswersforallquestions. Forthetestsubset,onlythedatasamplesare
released,whilethegroundtruthanswersremainconfidential. Toobtainthetestsubsetevaluation
results,oneneedstosubmitthepredictionstoMMBenchevaluationserver.
4 EvaluationStrategy
InMMBench,weproposeanewstrategythatyieldsrobustevaluationresultswithaffordablecosts.
Todealwiththefree-formoutputsofVLMs,weproposeutilizingstate-of-the-artLLMsasahelper
for choice extraction. We conduct extensive experiments to study the LLM-involved evaluation
procedure. TheresultswellsupporttheeffectivenessofGPT-4asachoiceextractor. Wefurtheradopt
anewevaluationstrategynamedCircularEval,whichfeedsaquestiontoaVLMmultipletimes
(withshuffledchoices)andchecksifaVLMsucceedsinallattempts. WithCircularEval,wedeliver
arigorousevaluationandmoreeffectivelydisplaytheperformancegapbetweenVLMs.
4.1 LLM-involvedChoiceExtraction
InourinitialattemptstoevaluateonMMBenchquestions,weobservedthattheinstruction-following
capabilitiesofVLMscanvarysignificantly. Thoughproblemsarepresentedasclearmultiple-choice
questions with well-formatted options, many VLMs still output the answers in free-form text2,
especiallyforVLMsthathavenotbeentrainedwithmultiple-choicequestionsorproprietaryVLMs
forgeneralpurposes(GPT-4v,Qwen-VL-Max,etc.). Extractingchoicesfromfree-formpredictionsis
straight-forwardforhumanbeings,butmightbedifficultwithrule-basedmatching. Tothisend,we
designauniversalevaluationstrategyforallVLMswithdifferentinstruction-followingcapabilities:
2Forexample,themodeloutputcanbethe meaningofchoice“A” ratherthan “A”.
6

Perfectly Aligned w. Human Mis-Aligned w. Human
Closed-Source LLMs Open-Source LLMs
Table1: StatisticsofIFcapabilitiesofVLMs. Wereport
theheuristicmatchingsuccessrateofVLMs,andtheaccuracy
beforeandafterLLM-basedchoiceextraction. In‘X+Y’,X
denotesthematching-basedaccuracy,Yindicatesthegainof
usingLLMasthechoiceextractor.
ModelName MatchRate DEVAcc ModelName MatchRate DEVAcc
MiniGPT4-7B 85.7 47.9+8.8 MiniGPT4-13B 84.8 52.1+8.7
InstructBLIP-7B 93.6 57.1+4.3 InstuctBLIP-13B 93.7 58.4+5.6 Figure 5: Alignment rates be-
IDEFICS-9B-Instruct 96.6 58.4+1.5 Qwen-VL-Chat 93.8 73.3+3.6 tween human and different
MiniCPM-V 95.2 70.9+4.5 VisualGLM-6B 64.8 39.9+23.2 LLMs. ‘chatgpt’ is ‘gpt-3.5-
GPT-4v 91.8 81.5+3.6 GeminiProVision 97.5 81.8+0.8 turbo’. Open-source LLMs are
Qwen-VL-Plus 77.4 64.5+15.0 Qwen-VL-Max 96.0 82.0+3.2 ‘chat’variants.
The original VL problem: Circular Evaluation
Q: How many apples are there in the image?
A.4; B. 3; C. 2; D. 1 GT: A
4 Passes in Circular Evaluation (choices with circular shift):
1. Q: How many apples are there in the image? Choices: A. 4; B. 3; C. 2; D. 1. VLM prediction: A. GT: A ✔
2. Q: How many apples are there in the image? Choices: A. 3; B. 2; C. 1; D. 4. VLM prediction: D. GT: D ✔
3. Q: How many apples are there in the image? Choices: A. 2; B. 1; C. 4; D. 3. VLM prediction: B. GT: C ✖
4. Q: How many apples are there in the image? Choices: A. 1; B. 4; C. 3; D. 2. VLM prediction: B. GT: B ✔
VLM failed at pass 3. Thus wrong.
Figure6: CircularEvalstrategy. InCircularEval,aproblemistestedmultipletimeswithcircular
shiftedchoicesandtheVLMneedstosucceedinalltestingpasses. Inthisexample,theVLMfailed
inpass3andthusconsideredfailedtheproblem.
Step1. MatchingPrediction. Initially,weattempttoextractchoicesfromVLMpredictionsusing
heuristicmatching. Weaimtoextractthechoicelabel(e.g.,A,B,C,D)fromtheVLM’soutput. If
successful,weusethisastheprediction. Ifnot,weattempttoextractthechoicelabelusinganLLM.
Step2.MatchingLLM’soutput. Ifstep1fails,wetrytoextractthechoicewithLLMs(gpt-4-0125
bydefault). WefirstprovideChatGPTwiththequestion,choices,andmodelprediction. Then,we
requestittoalignthepredictionwithoneofthegivenchoices,andsubsequentlyproducethelabelof
thecorrespondingoption. IftheLLMfindsthatthemodelpredictionissignificantlydifferentfrom
allchoices,weaskittoreturnapseudochoice‘Z’.Inexperiments,wefindthatforalmostallcases
weencountered,theLLMcanoutputavalidchoiceaccordingtotheinstruction. Foreachsample,we
comparethemodel’slabelprediction(afterGPT’ssimilarityreadout)withtheactualgroundtruth
label. Ifthepredictionmatchesthelabel,thetestsampleisconsideredcorrect.
4.2 LLMastheChoiceExtractor: AFeasibilityAnalysis
Instruction following (IF) capabilities of VLMs vary a lot. We conduct pilot experiments to
study the effectiveness of LLMs as the choice extractor. As a first step, we perform single-pass
inferenceonallMMBenchquestionswithVLMsinourevaluationcoreset(definedinSec.5.2).
WhilethereexistVLMsthatperfectlyfollowthemultiple-choiceformatandachievehighsuccess
rates(> 99%)inheuristicmatching, allproprietarymodelsandasignificantproportionofopen-
source VLMs failed to generate well-formatted outputs. In Table 1, we list the success rates of
differentVLMsinheuristicmatching3. AmongallVLMs,VisualGLMachievesthelowestmatching
successrate,whichismerely65%. ForthoseVLMs,incorporatingLLMsasthechoiceextractor
leadstosignificantchangeinthefinalaccuracy. AnothernoteworthythingisthattheIFcapability
and the overall multimodal understanding capability is not necessarily correlated. For example,
OpenFlamingov2[4]demonstratestopIFcapabilityamongallVLMs,whilealsoachievingoneof
theworstperformancesonMMBench(Table3).
3VLMsthatachieve>99%matchingratesarenotlisted,includingLLaVAseries,Yi-VLseries,mPLUG-
Owl2,OpenFlamingov2,andCogVLM-Chat.
7

QualityandstabilityofLLMChoiceExtractors. ForVLMpredictionsthatcannotbeparsedby
heuristicmatching,weadoptGPT-4asthechoiceextractor. Tovalidateitsefficacy,wefirstbuild
asubsetoftheinferencerecords. EachiteminthesetisapairofquestionsandVLMpredictions,
whichcannotbeparsedbystep-1matching. Wesample10%ofthosehardexamples(∼420samples),
andaskvolunteerstoperformmanualchoiceextractiononthesedatasamples. Suchannotations
enableustovalidatethechoiceextractionofLLMs,bymeasuringtheiralignmentrateswithhumans.
Figure5reportsthealignmentrates(extractedchoicesareexactlythesame)betweenLLMsand
humans. We find that a great number of LLMs can complete the task well and achieve decent
alignmentratewithhuman. AmongproprietaryLLMs,GPT-4achievesthehighestlevelofalignment
rate,whichis91.5%,whileGPT-3.5-TurboandQwen-Maxachievearound85%. Open-sourceLLMs
achievemorediversifiedperformanceonthechoicematchingtask. InternLM2-7B[45]achievesan
87%alignmentrateandsignificantlyoutperformsotheropen-sourceLLMsandGPT-3.5-Turbo. In
thefollowingexperiments,weadoptgpt-4-0125asthechoiceextractorduetoitssuperioralignment
capability. Meanwhile,wealsonotethattheslightdifferenceintop-performingLLMs’alignment
rateshaslittleeffectonthequantitativeperformanceofVLMs.
4.3 CircularEvalStrategy
InMMBench,theproblemsarepresentedasmultiple-choicequestions. Suchformulationposesan
evaluationchallenge: randomguessingwillleadto∼25%Top-1accuracyfor4-choicequestions,
potentiallyreducingthediscernibleperformancedifferencesamongVLMs. Besides, wenoticed
thatVLMsmayprefertopredictacertainchoiceamongallgivenchoices(Figure4),whichfurther
amplifiesthebiasinevaluation. Tothisend,weintroduceamorerobustevaluationstrategytermed
CircularEvaluation(orCircularEval). Underthissetting,eachquestionisfedtoaVLMN times
(N isthenumberofchoices). Eachtime,circularshiftingisappliedtothechoicesandtheanswerto
generateanewpromptforVLMs(exampleinFigure6). AVLMisconsideredsuccessfulinsolving
aquestiononlyifitcorrectlypredictstheanswerinallcircularpasses. Inpractice,onceaVLM
failsonacircularpasses,thereisnoneedtoinfertheremainingpasses,whichmakestheactualcost
ofCircularEvallessthanN×underpracticalscenarios. CircularEvalcanachieveagoodtrade-off
betweenrobustnessandcost.
5 EvaluationResults
5.1 ExperimentalSetup
Forthemainresults,weevaluatevariousmodelsbelongingtothreemajorcategoriesonMMBench:
(a)Text-OnlyGPT-4[37];(b)Open-SourceVLMsincludingmodelvariantsofOpenFlamingo[4],
MiniGPT4[56],InstructBLIP[11],LLaVA[32],IDEFICS[26],CogVLM[47],Qwen-VL[6],Yi-
VL[2],mPLUG-Owl[50],InternLM-XComposer[12],andMiniCPM-V[39];(c)ProprietaryVLMs
includingQwen-VL-[Plus/Max][6],Gemini-Pro-V[44],andGPT-4v[37]. Forafaircomparison,we
adoptthezero-shotsettingtoinferMMBenchquestionswithallVLMs,basedonthesameprompt.
ForallVLMs,open-endedgenerationisadoptedtoobtaintheprediction,and‘gpt-4-0125’isusedas
thechoiceextractor. IntheAppendix,weprovidedetailedinformationregardingthearchitectureand
theparametersizeforallOpen-SourceVLMsevaluatedinthispaper,aswellasadditionalresultsfor
moreVLMsundervarioussettings. WeconductalltheevaluationwithVLMEvalKit[14].
5.2 MainResults
CircularEval vs. VanillaEval. Before delving deeper into concrete evaluation results, we first
compare our CircularEval (infer a question over multiple passes, consistency as a must) with
VanillaEval (infer a question only once). In Table 2, we present the results with two evaluation
strategiesonMMBench-dev. FormostVLMs,switchingfromVanillaEvaltoCircularEvalleads
toasignificantdropinmodelaccuracy. Ingeneral,comparisonsunderCircularEvalcanreveala
moresignificantperformancegapbetweendifferentVLMs. LLaVA-v1.5-13Boutperformsits7B
counterpartby2.1%Top-1accuracyunderVanillaEval,whileamuchlargerperformancegap(4.7%
Top-1)isobservedunderCircularEval. Asaspecialcase,theperformanceofOpenFlamingov2drops
from36.7%toonly2.6%whenwemovefromVanillaEvaltoCircularEval. CircularEvalissucha
challengingsettingthatitevenmakesstate-of-the-artproprietaryVLMs(GPT-4v,Qwen-VL-Max,
8

Table2: CircularEvalvs.VanillaEval. WereporttheCircularEvalTop-1accuracyandaccuracy
drop(comparedtoVanillaEval)ofallVLMsonMMBench-dev.
VLM Circular AccChange VLM Circular AccChange VLM Circular AccChange
MiniGPT4-7B 32.7% -24.1% MiniGPT4-13B 37.5% -23.2% Yi-VL-6B 65.6% -9.8%
InstructBLIP-7B 37.4% -24.0% InstructBLIP-13B 40.9% -23.0% Yi-VL-34B 68.2% -9.5%
LLaVA-v1.5-7B 62.5% -11.2% LLaVA-v1.5-13B 67.2% -8.6% MiniCPM-V 64.8% -10.6%
IDEFICS-9B-Instruct 37.2% -22.6% LLaVA-InternLM2-20B 72.8% -7.0% Qwen-VL-Plus 62.9% -16.6%
VisualGLM-6B 36.1% -27.0% CogVLM-Chat-17B 62.4% -15.6% Qwen-VL-Max 76.4% -8.7%
Qwen-VL-Chat 59.5% -17.4% mPLUG-Owl2 63.5% -8.7% Gemini-Pro-V 70.9% -11.7%
OpenFlamingov2 2.6% -34.1% InternLM-XComposer2 79.1% -4.7% GPT-4v 74.3% -10.8%
etc.) sufferfrom∼10%Top-1accuracydrops. Inthefollowingexperiments,weadoptthemore
rigorousandwell-definedCircularEvalasourdefaultevaluationparadigm.
WeexhaustivelyevaluateallVLMsonallexistingleafabilitiesofMMBench. InTable3,wereport
themodels’overallperformanceandtheperformanceinsixL-2abilitiesonthetestsplit,namely
Coarse Perception (CP), Fine-grained Perception (single-instance, FP-S; cross-instance, FP-C),
Attribute Reasoning (AR), Logic Reasoning (LR), and Relation Reasoning (RR).4 The results
offervaluableinsightsintotheindividualstrengthsandlimitationsofeachVLMinmulti-modal
understanding.
PerformanceonMMBench-test. WefirstconductasanitycheckbyinferringMMBenchques-
tionswithGPT-4,usingtext-onlyinputs. Afterconductingtherigorousqualitycontrolparadigmin
Sec.3.2,GPT-4demonstratesarandom-leveloverallaccuracy.Amongopen-sourceVLMs,InternLM-
XComposer2[12]achievesthebestperformanceandsurpassotheropen-sourceorproprietarymodels
byalargemargin,w.r.t.theoverallscore,demonstratingitssuperiorabilityinmultimodalunder-
standing. After that, models adopting the architecture of LLaVA [33] (LLaVA series and Yi-VL
series)alsoshowcasestrongoverallperformance,whichisjustinferiortothestate-of-the-artclosed-
sourceGPT-4vandQwen-VL-Max. Withasmallparametersize(≤3B),MiniCPM-Vachievesover
60%Top-1accuracy,highlightingthepotentialofsmall-scaleVLMs. ModelsincludingMiniGPT,
IDEFICS,VisualGLM,andInstructBLIPdemonstratesignificantlyinferiorperformancecomparedto
otherVLMs,whileOpenFlamingov2showsrandom-levelperformanceduetothelackofinstruction
tuning.
LLMplaysavitalrole. Fromtheevaluationresults,wefindthatthelargelanguagemodel(LLM)
adoptedplaysavitalroleintheVLMperformance. Forinstance,allLLaVAseriesVLMs(v1.5-7B,
v1.5-13B,InternLM2-20B)adoptthesamevisionbackboneandaretrainedwiththesamemultimodal
corpus,whileswitchingtheLLMfromVicuna-v1.5[53]tothemorepowerfulInternLM2-20B[45]
leadstosteadyimprovementacrossallL-2capabilities(especiallysignificantforreasoningtasks).
ThescalingalsoholdsforvariantswithdifferentsizesfromthesameLLMfamily. Byadoptingthe
13BvariantofVicunaratherthanthe7Bvariant,VLMsintheMiniGPT,InstructBLIP,andLLaVA
v1.5seriesoutperformtheir7Bcounterpartsby8.3%,1.5%,and3.5%overallTop-1accuracieson
theMMBench-testsplit,respectively.
Performance on MMBench-CN. In Figure 7, we compare the performance of different VLMs
on MMBench and MMBench-CN. Most VLMs display a lower performance on MMBench-CN
comparedtotheresultsonMMBench,exceptOpenFlamingov2,VisualGLM,andQwen-VL-Plus.
ThedifferencemaybeattributedtotheunbalancedEnglishandChinesecorporausedinthepretraining
andinstruction-tuningofVLMsandtheircorrespondingLLMs. Wenoticethatmosttop-performing
VLMsonMMBenchalsodisplayoutstandingperformanceunderthebilingualcontext. Thelargest
EN-CNperformancegapformodelsthatachieve70+%Top-1accuracyonMMBenchisamere2%,
ForInternLM-XComposer2,theaccuracyonlydropsbylessthan1%whenevaluatedonMMBench-
CN.SuchanadvantagecanbeattributedtoutilizingLLMswithbetterbilingualcapabilitiesortuning
theVLMwithmorebalancedcross-languagemultimodalcorpora.
5.3 Fine-grainedAnalysis
Inthissection,wepresentmorefine-grainedanalysisbasedontheevaluationresults.
4Pleaserefertotheappendixformorefine-grainedresultsandMMBench-devsplitresults.
9

Table 3: CircularEval results on MMBench test set (L-2 abilities). Abbreviations adopted:
LR for Logical Reasoning; AR for Attribute Reasoning; RR for Relation Reasoning; FP-C for
Fine-grainedPerception(CrossInstance);FP-SforFine-grainedPerception(SingleInstance);CP
forCoarsePerception. Modelsaresortedbytheascendingorderofoverallaccuracy(intra-group).
Open-sourcemodelstaggedwith*incorporatein-housedatainmodeltraining.
| Model |     | Overall CP | FP-S FP-C | AR LR | RR  |
| ----- | --- | ---------- | --------- | ----- | --- |
LargeLanguageModels
| GPT-4-Turbo(0125)[37] |     | 2.9% 0.6% | 1.2% 4.1% | 3.7% 4.9% | 7.4% |
| --------------------- | --- | --------- | --------- | --------- | ---- |
OpenSourceVLMs
| OpenFlamingov2[4] |     | 2.3% 1.1%   | 3.5% 1.5%   | 5.3% 0.0%  | 2.7%  |
| ----------------- | --- | ----------- | ----------- | ---------- | ----- |
| MiniGPT4-7B[56]   |     | 30.5% 37.0% | 31.8% 17.2% | 49.8% 9.2% | 25.6% |
IDEFICS-9B-Instruct[26] 35.2% 48.3% 31.3% 29.6% 47.8% 11.4% 25.2%
| VisualGLM-6B[13]    |     | 35.4% 40.2% | 38.5% 26.2% | 47.8% 19.6% | 29.5% |
| ------------------- | --- | ----------- | ----------- | ----------- | ----- |
| InstructBLIP-7B[11] |     | 38.3% 46.7% | 39.0% 31.8% | 55.5% 8.7%  | 31.0% |
| MiniGPT4-13B[56]    |     | 38.8% 44.6% | 42.9% 23.2% | 64.9% 8.2%  | 32.9% |
InstructBLIP-13B[11] 39.8% 47.2% 42.9% 21.0% 60.4% 12.5% 38.8%
| Qwen-VL-Chat*[6]  |     | 60.9% 68.5% | 67.7% 50.2% | 78.0% 37.0% | 45.7% |
| ----------------- | --- | ----------- | ----------- | ----------- | ----- |
| MiniCPM-V[39]     |     | 61.4% 65.6% | 69.4% 51.3% | 70.6% 35.3% | 59.7% |
| LLaVA-v1.5-7B[32] |     | 63.4% 70.0% | 68.0% 57.7% | 77.6% 33.2% | 56.2% |
| mPLUG-Owl2[50]    |     | 63.5% 68.1% | 69.1% 55.8% | 78.4% 37.0% | 57.0% |
CogVLM-Chat-17B[47] 63.6% 72.8% 66.6% 55.4% 71.4% 33.7% 62.0%
| Yi-VL-6B*[2]       |     | 65.5% 72.8% | 72.9% 56.2% | 75.5% 41.3% | 55.4% |
| ------------------ | --- | ----------- | ----------- | ----------- | ----- |
| LLaVA-v1.5-13B[32] |     | 66.9% 73.1% | 72.4% 60.3% | 75.5% 35.9% | 65.5% |
| Yi-VL-34B*[2]      |     | 68.4% 72.0% | 78.0% 54.7% | 81.2% 38.6% | 68.2% |
LLaVA-InternLM2-20B[10] 72.3% 78.3% 76.6% 68.2% 78.4% 46.2% 69.4%
InternLM-XComposer2*[12] 78.1% 80.4% 83.5% 73.0% 83.7% 63.6% 74.4%
ProprietaryVLMs
| Qwen-VL-Plus[6]  |     | 64.6% 66.5% | 79.1% 50.2% | 73.9% 42.9% | 57.8% |
| ---------------- | --- | ----------- | ----------- | ----------- | ----- |
| Gemini-Pro-V[44] |     | 70.2% 70.0% | 78.9% 65.9% | 82.9% 46.2% | 65.9% |
| GPT-4v[37]       |     | 74.3% 77.6% | 73.8% 71.5% | 85.3% 63.6% | 68.6% |
| Qwen-VL-Max[6]   |     | 75.4% 74.8% | 87.2% 67.0% | 85.3% 54.9% | 70.5% |
Table 4: ‘Upper-bound’ Acc Es- Q. Who is the person  Q. Based on the interaction
|     |     |     | in this image? | between the individuals in the  |     |
| --- | --- | --- | -------------- | ------------------------------- | --- |
timation for Proprietary VLMs. A. Leonardo Dicaprio image, what is the most likely social
|     |     |     | B. Steve Jobs                   | relation or event being depicted?        |     |
| --- | --- | --- | ------------------------------- | ---------------------------------------- | --- |
|     |     |     | C. Jackie Chan                  | A. A formal diplomatic negotiation.      |     |
|     |     |     | D. Elon Musk                    | B. A casual meeting between              |     |
|     |     |     | Answer: A GPT-4v: I’m sorry, I  | friends. C. An organized sporting event. |     |
Model MMBench-test UpperBound can’t provide the  D. A military conflict.
|              |      |                                                   | identity of real people  | Answer: D                      |     |
| ------------ | ---- | ------------------------------------------------- | ------------------------ | ------------------------------ | --- |
| GPT-4v       | 74.3 | 76.2                                              | in images.               | Gemini-Pro-V reject to answer. |     |
|              |      | Figure8: ContentModerationCasesofProprietaryVLMs. |                          |                                |     |
| Gemini-Pro-V | 70.2 | 72.6                                              |                          |                                |     |
| Qwen-VL-Max  | 75.4 | 75.5                                              |                          |                                |     |
ContentModerationofProprietaryVLMs. Whenwetakeanin-depthlookatthepredictionsof
proprietaryVLMs,wenoticethatallofthemapplyexplicitcontentmoderation. GPT-4v,Gemini-
Pro-V,andQwen-VL-Maxrejectansweringin1.8%,1.6%,and0.1%ofcasesacrossallCircularEval
passes in MMBench, respectively. 74% of questions rejected by GPT-4v are related to celebrity
recognition (Figure 8), while no obvious rejection pattern is observed for Gemini-Pro-V. Under
CircularEval, such moderation has a negative impact on the evaluated accuracy. To estimate an
upper-boundperformance,weassumethatVLMscanperfectlyanswerallrejectedquestionsandre-
calculatetheaccuracy. Table4showsthatthecontentmoderationpolicyaffectstheMMBench-test
accuracybyupto2.4%,whichisnotasignificantchange.
10

2v
ognimalFnepO
B7-4TPGiniM B31-4TPGiniM B31-PILBtcurtsnI tcurtsnI-B9-SCIFEDI B7-PILBtcurtsnI B6-MLGlausiV B71-tahC-MLVgoC tahC-LV-newQ B7-5.1v-AVaLL V-MPCiniM 2lwO-GULPm B6-LV-iY B31-5.1v-AVaLL sulP-LV-newQ B43-LV-iY V-orP-inimeG B02-2MLI-AVaLL v4-TPG xaM-LV-newQ 2resopmoCX-MLI
80%
60%
40%
20%
0%
ycaruccA
tilpS
tseT
hcneBMM
Average
English
Chinese
Figure7: TheperformanceonthetestsplitofMMBenchandMMBench-CN. Modelsare
sortedwiththeascendingorderofaverageperformance. ILMstandsforInternLM.
CP FP-S FP-C AR LR RR
LLaVA-InternLM2-20B
Figure9: ProprietaryVLMsvs. Open-Sourceonesatafine-grainedlevel.
Proprietaryvs. Open-Source: Whatisthegap? Comparedtothevariedperformanceofopen-
sourceVLMs,mostproprietarymodelsdemonstratecompetitiveperformanceonMMBench. This
raisesaquestionwecareabout: areproprietarymodelsgenerallymorepowerful,ordoeachkind
ofmodeldisplayuniquestrengthsandweaknessesacrossdifferenttypesofability? Toanswerthis
question,weperformafine-grainedcomparisonofthreeproprietaryVLMsandLLaVA-InternLM2-
20B, the top-performing model trained on open-source datasets only, and visualize the result in
Figure9.Weobservethatproprietarymodelssignificantlyoutperformtheopen-sourceonesundertwo
majorscenarios: i)Structuralizedimage-textunderstanding,whichrequiresVLMstounderstand
complexcodes,tables,diagrams,orlayouts. ii)Tasksrequiringexternalknowledgetosolve,which
correspondtoabilitiesincludingcelebrityrecognition,physicalpropertyreasoning,naturalrelation
reasoning,etc. Meanwhile,proprietaryVLMsdonotdisplayadvantagesontaskscorrespondingto
otherperceptionorreasoningcapabilities.
HardcasesinMMBench. FormostVLMs,thefine-grainedaccuraciesvaryalotacrossdifferent
abilitycategories. ToprovideinsightsforfutureVLMoptimization,wefindthemaximumaccuracy
(A )acrossallevaluatedVLMsoneachL-3capability. SamplesbelongingtoL-3capabilities
max
withthelowestA arevisualizedinFigure10. Generally,wefindthatallexistingVLMshavethe
max
followinglimitations: 1. Pooratrecognizingthelow-levelfeaturesonvisualinputs,i.e.,theycannot
accuratelyrecognizeandcomparethebrightness,sharpness,contrastratio,orartifactsofimages.
2. Difficultyinunderstandingstructuralizedvisualinputsliketables,diagrams,orlayouts,evenfor
relativelysimplecaseslikeFigure10(b);3. Performbadlyonrecognizingorreasoningaboutthe
inter-objectspatialrelationships,eitherin2Dor3Dspace.
11

Q. The graph shows the meals
Q. Which image is the
purchased in a restaurant in one
|     | second brightest?  | day. What is the least popular  |
| --- | ------------------ | ------------------------------- |
A. upper-left
meal?
|     | B. upper-right  | A. Salad |
| --- | --------------- | -------- |
C. lower-left
B. Burger
|     | D. lower-right | C .   C h i c k en |
| --- | -------------- | ------------------ |
Answer: C A =61.5%
D .   P a s t a max
Answer: C
A max =61.3%
(a). Image Quality (b). StructralizedImage-Text Understanding
Q. What is the positional relationship between the two  Q. From the perspective of the
| shapes in the picture? |     | driver of the blue truck, in what  |
| ---------------------- | --- | ---------------------------------- |
A. The two shapes are positioned apart or separated  position is the person riding a bike
from each other.
| B. The two shapes are tangentially positioned or  |     | relative to the blue truck? |
| ------------------------------------------------- | --- | --------------------------- |
A. Left front
externally tangent to each other.
| C. The two shapes intersect with each other. |     | B. Right front |
| -------------------------------------------- | --- | -------------- |
D. One shape is contained within the other or there is  C. Right rear A =64.0%
max
| an inner shape enclosed by an outer shape. |           | D. Left rear |
| ------------------------------------------ | --------- | ------------ |
| Answer: C                                  | A =68.0%  | Answer: A    |
max
| (c). Spatial Relationship | (d). Physical Relation Reasoning |     |
| ------------------------- | -------------------------------- | --- |
Figure10: Hardexamplesthatbelongtothe4L-3abilitieswithlowestA . AllVLMshave
max
madethewrongpredictionforthevisualizedexamplesunderCircularEval.
6 Conclusion
WeintroduceMMBench,amulti-modalitybenchmarkthatperformsobjectiveevaluationforVLMs
withover3,000multiple-choicequestionscovering20abilitydimensions. Toproducerobustand
reliableevaluationresults,weintroduceanewevaluationstrategynamedCircularEval. Thestrategy
is much stricter than the vanilla 1-pass evaluation and can yield reliable evaluation results at an
affordable cost. Considering the limited instruction following ability of some VLMs, to yield
moreaccurateevaluationresults,weadditionallyadoptLLMstoextractchoicesfromthemodel’s
predictions. We comprehensively evaluate over 20 mainstream VLMs on MMBench, covering
differentarchitecturesandparametersizes. Theevaluationresultsprovidevaluableinsightsforfuture
improvements.
12

A MoreDetailsabouttheData
Inthissection,webeginbyprovidingadetaileddefinitionofeachleafability(L-3)andpresenta
collectionofvisualizationsamplesthataredirectlyrelatedtoeachleafability. Then,weenumerate
allthedatasourcesthatwereutilizedintheconstructionofMMBench.
A.1 DefinitionaboutEachLeafAbility
ImageStyle
Q: Which category does this Q: Which category does this
image belong to? image belong to?
A. OilPaiting A. OilPaiting
B. Sketch B. Sketch
C. Digitalart C. Digitalart
D. Photo D. Photo
GT:A GT:B
Q: Which of the following Q: Which of the following
ImageTopic captions best describes this captions best describes this
image? image?
A. A group of people playing A. A group of people playing
soccer in a field soccer in a field
B. A woman walking her dog on B. A woman walking her dog on
a beach a beach
C. A man riding a bicycle on a C. A man riding a bicycle on a
mountain trail mountain trail
D. A child playing with a ball in D. A child playing with a ball in
a park a park
GT:A GT:B
Imagescene
Q: What type of environment is Q: What type of environment is
depicted in the picture? depicted in the picture?
A. Home A. Home
B. shopping mall B. shopping mall
C. Street C. Street
D. forest D. forest
GT:A GT:C
ImageMood
Q: Which mood does this image Q: Which mood does this image
convey? convey?
A. Cozy A. Sad
B. Anxious B. Anxious
C. Happy C. Happy
D. Angry D. Angry
GT:C GT:A
ImageQuality
Q: Which imageismorebrightful? Q: which image is more colorful
A. Thefirstimage A. The first image
B. Thesecondimage B. The second image
GT:A GT: B
Figure11: CoarsePerception: Datasamples.
CoarsePerception
1. ImageStyle: Determinewhichtypeofimageitbelongsto,suchasphotos,paintings,CTscans,
etc.
2. ImageScene: Determinewhichenvironmentisshownintheimage,suchasindoors,outdoors,
forest,city,mountains,waterfront,sunnyday,rainyday,etc.
3. ImageEmotion: Determinewhichsubjectiveemotionisconveyedbytheoverallimage,suchas
cold,cheerful,sad,oroppressive.
4. ImageQuality: Determinetheobjectivequalityoftheimage,suchaswhetheritisblurry,bright
ordark,contrast,etc.
13

5. ImageTopic: Determinewhatthesubjectoftheimageis,suchasscenery,portrait,close-upofan
object,text,etc.
InFigure11,wevisualizedatasamplesbelongingtotheCoarsePerceptioncapability.
Attribute Recognition
Q: Whatistheshapeofthis Q: what is the colorof this
object? object?
A. Circle A. Purple
B. Triangle B. Pink
C. Square C. Gray
D. Rectangle D. Orange
GT:A GT:D
Celebrity Recognition
Q: Whoisthisperson Q: Whoisthisperson
A. DavidBeckham A. Benedict Cumberbatch
B. PrinceHarry B. Idris Elba
C. DanielCraig C. Ed Sheeran
D. TomHardy D. Harry Styles
GT:B GT:A
ObjectLocalization
Q: How many apples are there in
the image? And how many Q: Which corner is the juice?
bananas are there? A. Up
A. 4 apples and 2 bananas B. Down
B. 3 apples and 3 banana C. Left
C. 2 apples and 4 bananas D. Right
D. 4 apples and 1 bananas GT:D
GT:A
OCR Q: What does this picture want
to express?
Q: What does this outdoor A. We are expected to care for
billboard mean? green plants.
A. Smoking is prohibited here. B. We are expected to care for
B. Something is on sale. the earth.
C. No photography allowed C. We are expected to stay
D. Take care of your speed. positive.
GT:B D. We are expected to work
hard.
GT:D
Figure12: Fine-grainedPerception(single-instance): Datasamples.
Fine-grainedPerception(single-instance)
1. ObjectLocalization: Forasingleobject,determineitspositionintheimage(suchastop,bottom,
etc.),itsabsolutecoordinatesintheimage,countthenumberofobjects,andtheorientationofthe
object.
2. Attribute Recognition: Recognition of texture, shape, appearance characteristics, emotions,
category.
3. CelebrityRecognition: Recognitionofcelebrities,landmarks,andwell-knownobjects.
4. OCR:Recognitionoftext,formula,andsheetintheimage.
InFigure12,wevisualizedatasamplesbelongingtotheFine-grainedPerception(single-instance)
capability.
Fine-grainedPerception(cross-instance)
1. SpatialRelationship: Determinetherelativepositionbetweenobjectsinimage.
2. AttributeComparison: Compareattributesofdifferentobjectsinimage,suchasshape,color,
etc.
3. ActionRecognition: Recognizinghumanactions,includingposemotion,human-objectinterac-
tion,andhuman-humaninteraction.
14

Spatial Relationship
| Q:Which country is north of the  |     | Which country is the southernmost  |
| -------------------------------- | --- | ---------------------------------- |
| country circled in blue?         |     | of all the countries shown in the  |
| A.Laos                           |     | picture?                           |
| B.Thailand                       |     | A.Australia                        |
B.Indonesia
C.China
| D.Indonesia |     | C.China      |
| ----------- | --- | ------------ |
| GT:C        |     | D.NewZealand |
GT:B
Attribute Comparison
Q: Are the candies in the two jars
| Q: Are the two arrows in the same  |     | in the picture the same color? |
| ---------------------------------- | --- | ------------------------------ |
direction in the picture?
A. Same
| A. Same |     | B. Not the same |
| ------- | --- | --------------- |
B. Not the same
| C. Can't judge |     | C. Can't judge |
| -------------- | --- | -------------- |
GT:B
GT:B
Action Recognition
| Q: What kind of human behaviordoes  |           | Q: What kind of human behavior  |
| ----------------------------------- | --------- | ------------------------------- |
| this picture describe?              |           | does this picture describe?     |
| A. A man with a solemn expression,  |           | A. This is aXXXsmiles on their  |
| XXXdriving.                         |           | faces                           |
| B. A man is practicing his          |           | B. A man is XXXhis breathing    |
| skateboarding XXXskills.            |           | and inner thoughts.             |
| C. A group of XXXbreather from      |           | C. A musician XXXa classical    |
| work.                               |           | piece.                          |
| D. A family is XXX                  | clothing. | D. A family is XXXtogether.     |
| GT:A                                |           | GT:A                            |
Figure 13: Fine-grained Perception (cross-instance): Data samples. XXX indicates omitted
contentswhicharelessrelevanttothequestion.
InFigure13,wevisualizedatasamplesbelongingtotheFine-grainedPerception(cross-instance)
capability.
PhysicalPropertyReasoning
Q: The object shown in this figure:
| A. Is the hardest naturally occurring   |     | Q: The object shown in this figure:     |
| --------------------------------------- | --- | --------------------------------------- |
| substance on Earth.                     |     | A. Is one kind of metal that is liquid  |
| B. Conducts electricity well at room    |     | at the room temperature.                |
| temperature.                            |     | B. Can be easily dissolved in water.    |
| C. Is typically found in igneous rocks  |     | C. Has a low boiling point compared     |
| like basalt and granite.                |     | to other metals.                        |
| D. Has a low melting point compared     |     | D. Is attracted to magnets.             |
| to other minerals.                      |     | GT:A                                    |
GT:A
FunctionReasoning
| Q: What's the function of the  |     | Q: What's the function of the  |
| ------------------------------ | --- | ------------------------------ |
demonstrated object?
| A. Cut vegetables |     | demonstrated object? |
| ----------------- | --- | -------------------- |
A. Separating
| B. stir |     | B. Clamping |
| ------- | --- | ----------- |
C. Water purification
C. drill
| D. Boiling water |     | D. incise |
| ---------------- | --- | --------- |
GT:B
GT:A
IdentityReasoning
| Q: What's the profession of the people  |     | Q: What's the profession of the people  |
| --------------------------------------- | --- | --------------------------------------- |
in this picture?
| in this picture?  |     | A. Librarian |
| ----------------- | --- | ------------ |
A. Librarian
| B. radio host |     | B. accountant |
| ------------- | --- | ------------- |
C. radio host
| C. gardener |     | D. gardener |
| ----------- | --- | ----------- |
D. lawyer
E. lawyer
| GT:C      |                                  | GT:A |
| --------- | -------------------------------- | ---- |
| Figure14: | AttributeReasoning: Datasamples. |      |
15

AttributeReasoning
1. PhysicalPropertyReasoning: Predictthephysicalpropertyofanobject. Examples: hephysical
propertyofconcentratedsulfuricacidisthatitisvolatile,thephysicalpropertyofwaterisits
fluidity,etc.
2. FunctionReasoning: Predictthefunctionofanobject. Examples: thefunctionofabroomisto
sweepthefloor,thefunctionofaspatulaistocook,thefunctionofapenistowrite,etc.
3. IdentityReasoning: Predicttheidentityofaperson. Example: byobservingaperson’sclothing
andappearance,onemayinferhis/heroccupation.
InFigure14,wevisualizedatasamplesbelongingtotheAttributeReasoningcapability.
Social_Relation
Q: What can be the relationship
Q: What can be the relationship
between the two persons in this image?
between the two persons in this image?
A. Father and daughter
A. Father and daughter
B. Mother and son
B. Grandfather and granddaughter
C. Brother and sister
C. Brother and sister
D. Husband and wife
D. Husband and wife
GT:D
GT:B
NatureRelation
Q: In nature, what's the relationship Q: In nature, what's the relationship
between these two creatures? between these two creatures?
A. Predatory relationships A. Predatory relationships
B. Competitive relationships B. Competitive relationships
C. Parasitic relationships C. Parasitic relationships
D. Symbiotic relationship D. Symbiotic relationship
GT:B GT:D
PhysicalRelation
Q: Who is closer to the football in the Q: How many tennis balls are placed
image, the player in the black jersey on the tennis racket?
or the player in the green jersey? A. 1
A. Theplayerintheblackjersey B. 2
B. Theplayerinthegreenjersey C. 3
C. Theyareequallyclose D. 4
D. Itcannotbedetermined GT:C
GT:A
Figure15: RelationReasoning: Datasamples.
RelationReasoning
1. SocialRelation: Relationsinhumansocietyorrelationsdefinedfromthehumanperspective.
Examples: Inter-personrelations,suchasfatherandson,husbandandwife,friend,hostile,etc.
2. PhysicalRelation: Allrelationshipsthatexistinthephysicalworld,3Dspatialrelationshipsand
theconnectionsbetweenobjectsare.
3. NatureRelation:Otherabstractrelationshipsthatexistinnature.Examples:predation,symbiosis,
coexistence,etc.
InFigure15,wevisualizedatasamplesbelongingtotheRelationReasoningcapability.
LogicReasoning
1. StructuralizedImage-TextUnderstanding: Structuredunderstandingofimagesandtext,includ-
ingparsingthecontentofcharts(suchasthetrendsofmultiplebarsinabarchart),understanding
thecodeinanimage,etc.
2. FuturePrediction: Predictwhatwillhappeninthefuture. Examples: ifitisthunderinginthe
skynow,itcanbepredictedthatitwillrainsoon(physicalphenomenon);ifsomeoneraisestheir
fist,itmeanstheyaregoingtohitsomeone(eventoccurrence);ifsomeone’sfacebecomesserious,
itmeanstheyaregoingtogetangry(emotionalchange).
InFigure16,wevisualizedatasamplesbelongingtotheLogicReasoningcapability.
16

Future Prediction
|     | Q: What will happen next? |     |     | Q: What will happen next? |     |
| --- | ------------------------- | --- | --- | ------------------------- | --- |
A. the motorcyleis gonnago forward
|     | A. this person is gonnacry |     |     | B. the motorcyleis gonnacrash |     |
| --- | -------------------------- | --- | --- | ----------------------------- | --- |
B. this person is gonnalaugh
|     | C. this person is gonnaget mad |     |     | C. the motorcyleis gonnago  |     |
| --- | ------------------------------ | --- | --- | --------------------------- | --- |
backward
|     | D. both A,B, and C |     |     | D. both A,B, and C |     |
| --- | ------------------ | --- | --- | ------------------ | --- |
GT:A
GT:B
StructuralizedImage-textUnderstanding
|     | Q: According to this image, which  |                 |              | Q: According to this image, what  |     |
| --- | ---------------------------------- | --------------- | ------------ | --------------------------------- | --- |
|     | fruit did the most kids like?      |                 |              | hobby is liked the least?         |     |
|     | A. Orange                          |                 |              | A. Reading                        |     |
|     | B. Banana                          |                 |              | B. Singing                        |     |
|     | C. Pear                            |                 |              | C. Painting                       |     |
|     | D. Apple                           |                 |              | D. Dancing                        |     |
|     | GT:A                               |                 |              | GT:C                              |     |
|     | Figure16:                          | LogicReasoning: | Datasamples. |                                   |     |
A.2 DataSourcesofMMBench
Just as we introduce in Section 3.2 of the main paper, MMBench is mainly collected from the
Internnet(80%)andthevalidationsetofsomepublicdatasets(20%). Table5listsallthesesources
forimages,questionsandchoicesinMMBench.
| The      | source of (Q,C,I,A) | in MMBench | . Customize |       |                          |
| -------- | ------------------- | ---------- | ----------- | ----- | ------------------------ |
| Table 5: |                     |            |             | means | all of question, choices |
andanswerareconstructedbyus. Customize&selectionimpliesthatthesecomponentsareeither
constructedbyusorselectedfromtheoriginaldataset.
|     | ImageSource    | ProblemSource       |     | Number | Ratio |
| --- | -------------- | ------------------- | --- | ------ | ----- |
|     | ARAS[15]       | customize&selection |     | 76     | 2.4%  |
|     | CLEVR[24]      | customize&selection |     | 14     | 0.4%  |
|     | COCO[9]        | customize&selection |     | 179    | 5.6%  |
|     | KonIQ-10k[21]  | customize&selection |     | 32     | 1.0%  |
|     | LLaVA[33]      | customize           |     | 19     | 0.6%  |
|     | PISC[29]       | customize&selection |     | 15     | 0.5%  |
|     | Places[54]     | customize&selection |     | 59     | 1.8%  |
|     | ScienceQA[34]  | customize&selection |     | 156    | 4.8%  |
|     | ShapeWorld[25] | customize&selection |     | 20     | 0.6%  |
|     | TextVQA[42]    | customize&selection |     | 18     | 0.6%  |
|     | VSR[31]        | customize&selection |     | 19     | 0.6%  |
|     | W3CSchool[1]   | customize           |     | 20     | 0.6%  |
|     | Internet       | customize           |     | 2590   | 80.5% |
B MoreDetailsonMMBenchConstruction
Inthissectionweprovidemorequalitativeresultsonthequalitycontrolparadigmweadoptedto
constructMMBench,aswellasthepromptweusedforMMBench-CNtranslation.
‘Text-only’ question filtering. To filter out the ‘text-only’ questions (which can be answered
correctlywithtext-onlyinputsbyLLMs)fromMMBench. Weapplythreestate-of-the-artLLMs,
including GPT-4 [37], Gemini-Pro [44], and Qwen-Max [5] to infer the questions with text-only
inputsunderCircularEval. IfmorethantwoLLMsanswerthequestioncorrectly,thequestionwillbe
manuallycheckedandremovedifitisunqualified. InFigure17(a),wevisualizesomeunqualified
questionsfilteredoutbythisapproach.
‘Wrong’questionfiltering. Duringpreliminarystudy,wealsonoticethatsomedatasamplesin
MMBenchmightbewrong,duetoambiguousquestionsoroptions,repeatedoptions,orincorrect
answers. Tofilteroutthesewrongsamples, weinferMMBenchquestionswiththreeproprietary
VLMs(GPT-4v,Gemini-Pro-V,Qwen-VL-Max)andtwoopensourceVLMs(InternLM-XComposer2
17

| Q. Which part of an apple tree  |     |     | Q. The object shown in this figure:                   |     |     |
| ------------------------------- | --- | --- | ----------------------------------------------------- | --- | --- |
| might grow into a new tree?     |     |     | A. Is a bluish-white metal that is commonly used in   |     |     |
| Hint: This paradigm shows the   |     |     | galvanizing and as an alloy in brass and other metals |     |     |
B. Has a relatively low melting point of around 419°C
| life cycle of an apple tree.  |     |     | C. Is an essential micronutrient for humans and many  |     |     |
| ----------------------------- | --- | --- | ----------------------------------------------------- | --- | --- |
A. aseed
other organisms
| B. aleaf                     |     |     | D. All of the options are correct.       |     |     |
| ---------------------------- | --- | --- | ---------------------------------------- | --- | --- |
| Answer: A (common knowledge) |     |     | Answer: D (can be inferred from options) |     |     |
| Source:ScienceQA             |     |     | Source: Internet                         |     |     |
(a). Text-Only questions filtered out
| Q. What’s the function of the  |     |     | Q. What’sthefunctionofthe                |     |     |
| ------------------------------ | --- | --- | ---------------------------------------- | --- | --- |
| demonstrated object?           |     |     | demonstratedobject?                      |     |     |
| A. Cooking                     |     |     | A. Celebrate someone’s birthday          |     |     |
| B. CookSoup                    |     |     | B. Celebrating a wedding                 |     |     |
| C. Fry                         |     |     | C. Asanitary facility used for excretion |     |     |
| D.Steam                        |     |     | D. Offering a variety of drink           |     |     |
| Answer: A (ambiguous options)  |     |     | Answer: A (ambiguous options)            |     |     |
| Source:Internet                |     |     | Source:COCO                              |     |     |
(b). Wrong questions filtered out
| Figure17: | UnqualifiedsamplesfilteredoutinMMBench. |     |     |     |     |
| --------- | --------------------------------------- | --- | --- | --- | --- |
andLLaVA-v1.5-13B).IfnoVLMcanansweraquestioncorrectlyunderCircularEval,thequestion
will then be manually checked. In Figure 17(b), we visualize wrong samples filtered out by the
approach.
| Q. Is this photo taken in a  |                        | Q. If the owner of this room wants to  |                                  | Q. Whatcanbeinferredabout       |                               |
| ---------------------------- | ---------------------- | -------------------------------------- | -------------------------------- | ------------------------------- | ----------------------------- |
|                              |                        | add a new piece of furniture, what     |                                  | thegroupofpeoplesittingon       |                               |
| place of escalator indoor?   |                        | m a t e r i a l   s h o u l            | d   t h e y   c h o o s e   t o  | t h e s t r e e t ?             |                               |
| P l e a s e   a n s w e      | r   y e s   o r   n o. |                                        |                                  | A . T h e y   a r e   h o m     | e l e s s                     |
| A n s w e r . Y e s .        |                        | m a i n t a i n   t h e   o            | v e r a l l   t h e m e ?        | B . T h e y   a r e   s t r e   | e t p e r f o r m er s        |
| ( t h e   p h o t o   f e    | a t u r e s   a n      | A . W o o d     B .   W                | i c k e r                        | C .   T h e y   a r e   w a i   | t i n g   f o r   p a r a d e |
| o u t d o o r   s c e n      | e )                    | C .   P l a s t i c     D .   M        | e t a l                          | D .   T h e y   a r e   t o u r | i s t s                       |
| B e n c h m a r k :          | M M E                  | A n s w e r :   D   ( i n              | c o r r e c t   a n s w e r )    | A n s w e r :   D   ( w r o     | n g q u e s t i o n )         |
|                              |                        | B e n c h m a r k :   S                | E E D B e n c h                  | B e n c h m a r k :   S E E     | D B e n c h                   |
Figure18: Unqualifiedsamplesinotherbenchmarkscanalsobedetectedbyourqualitycontrol
paradigms.
TheUniversalityoftheQualityControlParadigm. Thequalitycontrolparadigmadoptedby
MMBenchisgeneralandcanalsobeappliedtootherbenchmarkstoimprovethequality. Tosupport
thisclaim,weapplythequalitycontrolparadigmtootherpopularmultimodalevaluationbenchmarks
(likeMME[17]andSEEDBench[27])andtrytodetectthelow-qualitysamples. Wefindthatour
qualitycontrolparadigmcanalsosuccessfullydetectandfilteroutunqualifiedsamplesfromthese
benchmarks. SomedetectedsamplesarevisualizedinFigure18.
MMBench-CNTranslation. InFigure19,weprovidethepromptweadoptedforMMBench-CN
translation,whichincludeinstructionsandseveralin-contextexamples. Alltranslationsgeneratedby
GPT-4willbefurthermanuallyverfiedtoensurethecorrectness.
C MoreDetailsonLLM-basedChoiceExtraction
FailureCasesofHeuristicMatching. InFigure20,wedisplaysomefailurecasesofheuristic
matchingofthestate-of-the-artVLMGPT-4v. Basically,suchfailuremayoccurwhentheVLM:i)
rejectsorisnotcapabletoanswerthegivenquestion; ii)answersthequestionindifferentwords
ratherthanthecorrectchoice;iii)providesananswerwithmultiplechoicelabels(A,B,C,etc. )
included.
ThepromptforLLM-basedChoiceExtraction. InFigure21,weprovidethepromptweadopted
for LLM-based choice extraction. In-context examples are included to improve the instruction-
followingcapabilityoftheLLMadopted.
PerformanceEvaluatedwithOtherChoiceExtractors. InTable6,welisttheMMBench-dev
performanceobtainedwithdifferentchoiceextractors,includingGPT-4(0125),GPT-3.5-Turbo(0613
and0125),andInternLM2-7B[45]. VLMswithhighsuccessrate(>99%)inheuristicmatchingare
skipped. Fromthetable,weseethatadoptingdifferentchoiceextractorswillnotleadtosignificant
differentevaluationresults. VisualGLMdisplaysthelargestrangeacrossallchoiceextractors,which
isaround1.4%. Fortop-performingproprietaryVLMs(GPT-4v,Gemini-Pro-V,etc.),thegapisat
most0.3%.
18

B.1MMBench-CNTranslation
你是一个翻译助手，你的任务是帮我把下面的英文题目及选项翻译成中文，并保持
完全一样的含义。你仅需要翻译文本中的英文内容，不需要翻译其他语言的内容，
请只翻译给定内容，不要丢失/修改/添加内容。对于文本中的专有名词，符号，代
码，或是人名等，请依然保持英文，不需要翻译。我会以“json”格式给出题目及选
项的内容，你需要把翻译后的中文内容以“json”格式返回给我。
例1：
英文:
{"Q": "Whichofthefollowingwaspartoftheroleofadeaconess? ","A": "Ministeringto
| thesick","B": | "Preparingwomenforbaptism","C": | "Prayingforthesuffering"} |     |
| ------------- | ------------------------------- | ------------------------- | --- |
中文:
| "以下哪项是女执事的职责之一？", |     | "照顾病人", | "为女性准备洗礼", |
| ----------------- | --- | ------- | ---------- |
| {"Q":             |     | "A":    | "B":       |
"为受苦的人祷告"}
"C":
例2：
英文:
{"Q":"Whichcanbetheassociatedtextwiththisimagepostedontwitter?","A":"Locatedin
BomeCounty,NyingchiCity,TibetofChina,theYigongIronMountainisalwayssurrounded
"夏天所有季节中最闪耀的季节阳光明媚，
| bycloudsandmistduringsummer.", | "B": |     |     |
| ------------------------------ | ---- | --- | --- |
万物清明泰山向人们展现的初夏之景处处充满着诗情画意",
"C": "Giant logs and
strippedtreesonRialtoBeachintheOlympicNationalPark. #beach#wawx#blackandwhite
@yourtake","D":"MadisonFallsinOlympicNationalPark,WA[OC][3024x4032]#nature"}
中文:
| "与这张推特上图片配套的推文是什么？",    |     | "坐落在中国西藏自治区林芝 |              |
| ----------------------- | --- | ------------- | ------------ |
| {"Q":                   |     | "A":          |              |
| 市波密县的易贡铁山，在夏季总是被云雾环绕。", |     |               | "夏天所有季节中最闪耀的 |
"B":
季节阳光明媚，万物清明泰山向人们展现的初夏之景处处充满着诗情画意",
"C":
| "奥林匹克国家Rialto | 沙滩上的巨木与被剥皮的树木。#beach#wawx#blackandwhite |     |     |
| ------------- | --------------------------------------- | --- | --- |
@yourtake","D": "Madison瀑布，奥林匹克国家公园，WA[OC][3024x4032]#nature"}
请翻译：
英文:
{TheEnglishquestionpresentedinthejsonformat}
中文:
| Figure19: | AnexamplepromptofChinesesinglechoicewithreasoning. |     |     |
| --------- | -------------------------------------------------- | --- | --- |
Failure Cases of Heuristic Matching
|     | Q. How many apples are there in the image? And how       |     | Q. Whatbandisthis?                                               |
| --- | -------------------------------------------------------- | --- | ---------------------------------------------------------------- |
|     | many bananas are there?                                  |     | A.The Beatles  B. Sex Pistols  C. Oasis  D. Guns N’ Roses        |
|     | A. 2 apples and 2 bananas  B. 3 apples and 3 bananas     |     | Answer: B                                                        |
|     | C. 2 apples and 4 bananas  D. 4 apples and 1 banana      |     | GPT-4v: The image you've provided appears to be in black         |
|     | Answer: A                                                |     | and white and shows a band performing live. However, I'm         |
|     | GPT-4v: In the image, there is 1 apple and 1 banana.     |     | unable to identify the specific band or confirm their identity.  |
|     | However, this does not match any of the options          |     | To answer your question based on the options provided, I         |
|     | provided. If I must choose from the given options, none  |     | would need more context or information that is not               |
|     | of them accurately describe the contents of the image.   |     | available in the image. If you have any other questions or       |
need information on the bands listed, feel free to ask!
Q. Which solution has a higher concentration of purple particles?
|     | A.Neither; their concentrations are the same   |     | Q. Who is sitting in the middle? |
| --- | ---------------------------------------------- | --- | -------------------------------- |
|     | B. Solutions A          C. Solution B          |     | A.The little girl.               |
B.Theman.
Answer: C
|     | GPT-4v: The concentration of a solution is determined by the amount  |     | C.The woman.                     |
| --- | -------------------------------------------------------------------- | --- | -------------------------------- |
|     | of solute present in a given volume of solvent. In the image, both   |     | Answer: A                        |
|     | Solution A and Solution B have the same volume of solvent (25 mL),   |     | GPT-4v:Thelittlegirl is sitting  |
|     | but Solution B has more purple particles (solute) than Solution A.   |     | in the middle.                   |
Therefore, Solution B has a higher concentration of purple particles.
Figure20: FailurecasesofGPT-4vduringexactmatching.
LLM-based sementic matching is generally helpful. To demonstrate that LLMs can be a
general tool for semantic matching, we also validate the LLM-involved evaluation paradigm on
existingmulti-modalitytasks,includingGQA[23],OK-VQA[35],andText-VQA[42]. Giventhe
ground-truthanswer,weuseGPT-3.5-TurbotomeasurethesimilaritybetweenVLM’sprediction5.
Foreachbenchmark,werandomlyselect1000testingsamplesandevaluatewithexactmatch(the
traditionalparadigm)andChatGPT-basedmatch,respectively,andlisttheresultsinTable7.Basically,
ChatGPT-basedevaluationdemonstratesthesametrendcomparedtotheexact-matchaccuracyon
all tasks. On GQA, two algorithms demonstrate very close performance under ChatGPT-based
5Thesimlarityscoreisanintegerin[1,5].1meanscompletelywrong,while5meanscompletelycorrect.
19

C.1PromptforChoiceExtraction
You are an AI assistant who will help me to match an answer with several options of a
single-choicequestion. Youareprovidedwithaquestion,severaloptions,andananswer,
and you need to find which option is most similar to the answer. If the meaning of all
optionsaresignificantlydifferentfromtheanswer,outputZ.Youshouldonlydothematching
basedexactlyontheliteralmeaningoftheoptionsandanswer. Youshouldnotperformany
externalinferencebasedonyourknowledgeduringthematching. Yourshouldoutputasingle
uppercasecharacterinA,B,C,D(iftheyarevalidoptions),andZ.
Example1:
Question: Whatisthemainobjectinimage?
Options: A.teddybearB.rabbitC.catD.dog
Answer: acuteteddybear
Youroutput: A
Example2:
Question: Whatisthemainobjectinimage?
Options: A.teddybearB.rabbitC.catD.dog
Answer: Spider
Youroutput: Z
Nowit’syourturn:
Question: {question}
Options: {options}
Answer: {answer}
Youroutput:
Figure21: ThepromptusedforchoiceextractiononMMBench. TheChinesetranslationofthis
promptisadoptedforMMBench-CNchoiceextraction.
Table6: MMBench-devaccuracieswithdifferentchoiceextractorsunderCircularEval.
|                         | Exact    | GPT-4-Turbo | GPT-3.5-Turbo | GPT-3.5-Turbo |              |
| ----------------------- | -------- | ----------- | ------------- | ------------- | ------------ |
| VLM                     |          |             |               |               | InternLM2-7B |
|                         | Matching | (0125)      | (0613)        | (0125)        |              |
| MiniGPT4-7B[56]         | 26.0     | 32.7        | 33.1          | 33.0          | 32.9         |
| IDEFICS-9B-Instruct[26] | 36.0     | 37.2        | 37.2          | 37.2          | 37.2         |
| InstructBLIP-7B[11]     | 34.8     | 37.4        | 37.5          | 37.5          | 37.7         |
| VisualGLM-6B[13]        | 19.4     | 36.1        | 37.5          | 37.5          | 36.1         |
| MiniGPT4-13B[56]        | 30.7     | 37.5        | 37.8          | 37.8          | 37.6         |
| InstructBLIP-13B[11]    | 36.6     | 40.9        | 41.1          | 41.0          | 41.4         |
| Qwen-VL-Chat[6]         | 56.7     | 59.5        | 59.8          | 59.4          | 59.8         |
| Qwen-VL-Plus[6]         | 43.7     | 62.9        | 62.6          | 61.9          | 63.2         |
| MiniCPM-V[39]           | 57.6     | 64.8        | 64.7          | 64.6          | 64.7         |
| Gemini-Pro-V[44]        | 70.4     | 70.9        | 70.9          | 70.9          | 70.8         |
| GPT-4v[37]              | 71.8     | 74.3        | 74.6          | 74.6          | 74.6         |
| Qwen-VL-Max[6]          | 72.9     | 76.4        | 76.5          | 76.2          | 76.5         |
evaluation. Infurtherinvestigation,wefindthereasonisthatChatGPTsucceedsinmatchingslightly
differentanswers(comparedtoGT)generatedbyMiniGPT-4,whileexactmatchingfails(examples
inTable8).
D EvaluationSettingsandResults
InSection5.2ofthemainpaper,wegivetheresultsofdifferentmodelsonthetestsplitofMMBench
andMMBench-CN.Inthissection,wewillintroducethedetailedevaluationsettingonMMBench,
andprovidemoreevaluationresults.
20

Table7: LLM-basedMatchingvsExactMatching. ApreliminarystudyonVQAbenchmarks.
Accuracy is the success rate of answers being exactly matched with the groundtruth. For each
sample,GPTscoreisanintegern∈[1,5],indicatingthesimilaritybetweenanswerandgroundtruth.
Weadoptgpt-3.5-turbo-0613asthematchingLLMandreporttheaverageGPTscorefortesting
samples.
Dataset GQA[23] OK-VQA[35] Text-VQA[42]
Model Flamingo MiniGPT-4 Flamingo MiniGPT-4 Flamingo MiniGPT-4
Accuracy 33.6% 22.4% 42.6% 21.9% 22.9% 9.8%
AverageGPTscore 2.75 2.74 2.79 1.97 1.92 1.54
Table 8: MiniGPT-4 GQA answers (different from GT) that can be matched by ChatGPT-based
evaluation.
Question GroundTruth MiniGPT-4Answer GPTScore
Onwhichsideofthepictureistheleatherbag? right leatherbagisonrightside 5
Whoiswearingashirt? girl womaniswearingshirt 5
Whatisthepenmadeof? plastic penismadeofplastic## 5
Whatdoesthemalepersonrideon? bike bicycle 4
Isthemicrowavesilverandrectangular? Yes microwaveissilverandrectangular 5
Howdoesthesilverlampappeartobe,onoroff? off silverlampappearstobeoff## 5
D.1 EvaluationSettings
Unless stated otherwise, all results presented in this paper adhere to the conventional zero-shot
evaluationsetting. Wehavealsoattemptedtoassessthesemodelswithfew-shotandchain-of-thought
evaluations. However,noencouragingresultsareobserved. Belowweprovidethepromptweused
forevaluatingaVLMunderthezero-shotsettingonMMBench.
D.1PromptTemplateforZero-shotInference.
Hint: xxx[optional]
Question: xxx
A.xxx
B.xxx
C.xxx[optional]
D.xxx[optional]
Pleaseselectthecorrectanswerfromtheoptionsabove.
Figure22: Theprompttemplateadoptedforzero-shotinference.
D.2 ModelSettings
InTable9,weprovidedetailsofallopen-sourcemodelsevaluatedinMMBench,includingseveral
additionalmodelsthatdonotfitthespaceofthemainarticle.
D.3 MoreResults
Inthissection,wegivemoredetailedresultsabouttheperformanceofdifferentmodelsonMMBench
andMMBench-CN.Wepresentthedetailedevaluationresultsof30differentVLMs(someofthem
donotappearinthemainpaperduetolimitedspace). FordetailedresultsoneachL-3ability,seethe
separatesheetinthesupplementarymaterials.
21

Table9: DetailsoftheevaluatedOpen-SourceVLMs.
| VLM                      | LanguageBackbone | VisionBackbone    | OverallParameters |
| ------------------------ | ---------------- | ----------------- | ----------------- |
| OpenFlamingov2[4]        | MPT7B            | CLIPViT-L/14      | 9B                |
| MiniGPT-4-7B[56]         | Vicuna7B         | EVA-G             | 8B                |
| IDEFICS-9B-Instruct[26]  | LLaMA7B          | CLIPViT-H/14      | 9B                |
| VisualGLM-6B[13]         | ChatGLM6B        | EVA-CLIP          | 7B                |
| InstructBLIP-7B[11]      | Vicuna7B         | EVA-G             | 8B                |
| MiniGPT-4-13B[56]        | Vicuna13B        | EVA-G             | 14B               |
| PandaGPT[43]             | Vicuna13B        | ImageBindViT-H/14 | 14B               |
| InstructBLIP-13B[11]     | Vicuna13B        | EVA-G             | 14B               |
| IDEFICS-80B-Instruct[26] | LLaMA65B         | CLIPViT-H/14      | 80B               |
| Qwen-VL-Chat[6]          | Qwen7B           | ViT-G/16          | 10B               |
| MiniCPM-V[39]            | MiniCPM2.4B      | SigLip-400M       | 3B                |
| LLaVA-v1.5-7B[32]        | Vicuna7B         | CLIPViT-L/14      | 7B                |
| mPLUG-Owl2[49]           | LLaMA27B         | CLIPViT-L/14      | 8B                |
| CogVLM-Chat-17B[47]      | Vicuna7B         | EVA2-CLIP-E       | 18B               |
| ShareGPT4V-7B[8]         | Vicuna7B         | CLIPViT-L/14      | 7B                |
| Yi-VL-6B[2]              | Yi-6B            | CLIPViT-H/14      | 7B                |
| LLaVA-InternLM-7B[10]    | InternLM7B       | CLIPViT-L/14      | 9B                |
ShareGPT4V-13B[8]
|                         | Vicuna13B     | CLIPViT-L/14 | 13B |
| ----------------------- | ------------- | ------------ | --- |
| LLaVA-v1.5-13B[32]      | Vicuna13B     | CLIPViT-L/14 | 13B |
| Yi-VL-34B[2]            | Yi34B         | CLIPViT-H/14 | 35B |
| OmniLMM-12B[38]         | Zephyr-7B-β   | EVA-02-5B    | 12B |
| Monkey-Chat[30]         | Qwen7B        | ViTBigG      | 10B |
| InternLM-XComposer[52]  | InternLM-7B   | EVA-G        | 9B  |
| LLaVA-InternLM2-7B[10]  | InternLM2-7B  | CLIPViT-L/14 | 9B  |
| LLaVA-InternLM2-20B[10] | InternLM2-20B | CLIPViT-L/14 | 23B |
| InternLM-XComposer2[12] | InternLM2-7B  | CLIPViT-L/14 | 9B  |
22

Table10: CircularEvalresultsonMMBench-devset(L-2abilities). Open-sourcemodelstagged
with*incorporatein-housedatainmodeltraining.
| Model | Overall CP | FP-S FP-C | AR LR | RR  |
| ----- | ---------- | --------- | ----- | --- |
OpenSourceVLMs
| OpenFlamingov2[4] | 2.6% 0.8%   | 4.5% 1.1%   | 5.5% 0.0%   | 3.4%  |
| ----------------- | ----------- | ----------- | ----------- | ----- |
| MiniGPT4-7B[56]   | 32.7% 38.4% | 39.1% 20.7% | 49.4% 10.5% | 22.4% |
| VisualGLM-6B[13]  | 36.1% 40.3% | 43.3% 19.6% | 49.4% 16.9% | 33.9% |
IDEFICS-9B-Instruct[26] 37.2% 50.6% 37.7% 30.2% 51.8% 4.8% 25.3%
| InstructBLIP-7B[11] | 37.4% 46.4% | 47.1% 23.5% | 51.2% 8.1% | 24.7% |
| ------------------- | ----------- | ----------- | ---------- | ----- |
| MiniGPT4-13B[56]    | 37.5% 44.2% | 48.4% 16.8% | 57.3% 6.5% | 30.5% |
InstructBLIP-13B[11] 40.9% 48.6% 52.2% 18.4% 56.7% 5.6% 39.7%
| PandaGPT[43] | 41.6% 56.1% | 34.6% 34.6% | 53.7% 13.7% | 38.5% |
| ------------ | ----------- | ----------- | ----------- | ----- |
IDEFICS-80B-Instruct[26] 42.3% 54.7% 48.1% 24.6% 57.3% 8.9% 34.5%
| Qwen-VL-Chat*[6] | 59.5% 70.7% | 69.9% 49.7% | 69.5% 25.0% | 44.3% |
| ---------------- | ----------- | ----------- | ----------- | ----- |
CogVLM-Chat-17B[47] 62.4% 69.6% 70.6% 56.4% 67.1% 29.0% 59.2%
| LLaVA-v1.5-7B[32] | 62.5% 71.3% | 70.6% 55.9% | 70.7% 25.8% | 55.7% |
| ----------------- | ----------- | ----------- | ----------- | ----- |
| mPLUG-Owl2[50]    | 63.5% 72.9% | 70.2% 53.6% | 70.7% 29.8% | 60.3% |
| MiniCPM-V[39]     | 64.8% 71.0% | 75.1% 52.5% | 72.0% 30.6% | 64.9% |
| Yi-VL-6B*[2]      | 65.6% 72.7% | 73.7% 54.7% | 73.2% 32.3% | 65.5% |
| ShareGPT4V-7B[8]  | 66.2% 77.3% | 75.1% 57.5% | 68.3% 25.8% | 63.8% |
| ShareGPT4V-13B[8] | 67.0% 75.1% | 77.9% 58.1% | 68.9% 35.5% | 61.5% |
LLaVA-InternLM-7B[10] 67.0% 75.7% 72.7% 57.5% 71.3% 37.1% 66.7%
| LLaVA-v1.5-13B[32] | 67.2% 74.0% | 75.1% 59.2% | 68.9% 38.7% | 66.7% |
| ------------------ | ----------- | ----------- | ----------- | ----- |
| Yi-VL-34B*[2]      | 68.2% 75.7% | 73.0% 55.9% | 75.6% 39.5% | 70.7% |
| Monkey-Chat[30]    | 68.8% 72.9% | 79.2% 58.1% | 79.3% 42.7% | 62.6% |
| OmniLMM-12B*[38]   | 69.7% 75.1% | 79.6% 61.5% | 73.8% 37.1% | 69.5% |
LLaVA-InternLM2-7B[10] 71.6% 79.8% 77.2% 62.0% 74.4% 41.1% 74.1%
LLaVA-InternLM2-20B[10] 72.8% 80.1% 75.1% 68.2% 73.8% 46.0% 76.4%
InternLM-XComposer*[52] 73.9% 79.6% 81.7% 65.4% 84.8% 39.5% 72.4%
InternLM-XComposer2*[12] 79.1% 83.4% 84.4% 68.7% 83.5% 58.1% 82.8%
ProprietaryVLMs
| Qwen-VL-Plus[6]  | 62.9% 67.1% | 78.9% 53.1% | 71.3% 28.2% | 54.6% |
| ---------------- | ----------- | ----------- | ----------- | ----- |
| Gemini-Pro-V[44] | 70.9% 71.3% | 81.7% 62.0% | 78.7% 47.6% | 70.7% |
| GPT-4v[37]       | 74.3% 78.5% | 72.3% 66.5% | 82.9% 67.7% | 73.6% |
Qwen-VL-Max[6]
|     | 76.4% 76.2% | 87.2% 69.3% | 78.7% 55.6% | 78.7% |
| --- | ----------- | ----------- | ----------- | ----- |
23

Table11:CircularEvalresultsonMMBench-testset(L-2abilities). Open-sourcemodelstagged
with*incorporatein-housedatainmodeltraining.
| Model | Overall CP | FP-S FP-C | AR LR | RR  |
| ----- | ---------- | --------- | ----- | --- |
OpenSourceVLMs
| OpenFlamingov2[4] | 2.3% 1.1%   | 3.5% 1.5%   | 5.3% 0.0%  | 2.7%  |
| ----------------- | ----------- | ----------- | ---------- | ----- |
| MiniGPT4-7B[56]   | 30.5% 37.0% | 31.8% 17.2% | 49.8% 9.2% | 25.6% |
IDEFICS-9B-Instruct[26] 35.2% 48.3% 31.3% 29.6% 47.8% 11.4% 25.2%
| VisualGLM-6B[13]    | 35.4% 40.2% | 38.5% 26.2% | 47.8% 19.6% | 29.5% |
| ------------------- | ----------- | ----------- | ----------- | ----- |
| InstructBLIP-7B[11] | 38.3% 46.7% | 39.0% 31.8% | 55.5% 8.7%  | 31.0% |
| MiniGPT4-13B[56]    | 38.8% 44.6% | 42.9% 23.2% | 64.9% 8.2%  | 32.9% |
| PandaGPT[43]        | 39.7% 51.9% | 29.5% 27.3% | 62.0% 19.0% | 38.0% |
InstructBLIP-13B[11] 39.8% 47.2% 42.9% 21.0% 60.4% 12.5% 38.8%
IDEFICS-80B-Instruct[26] 40.9% 54.6% 38.1% 29.6% 52.7% 16.8% 34.9%
| Qwen-VL-Chat*[6]  | 60.9% 68.5% | 67.7% 50.2% | 78.0% 37.0% | 45.7% |
| ----------------- | ----------- | ----------- | ----------- | ----- |
| MiniCPM-V[39]     | 61.4% 65.6% | 69.4% 51.3% | 70.6% 35.3% | 59.7% |
| LLaVA-v1.5-7B[32] | 63.4% 70.0% | 68.0% 57.7% | 77.6% 33.2% | 56.2% |
| mPLUG-Owl2[50]    | 63.5% 68.1% | 69.1% 55.8% | 78.4% 37.0% | 57.0% |
CogVLM-Chat-17B[47] 63.6% 72.8% 66.6% 55.4% 71.4% 33.7% 62.0%
| ShareGPT4V-7B[8] | 64.6% 72.2% | 68.7% 59.6% | 72.7% 34.8% | 60.5% |
| ---------------- | ----------- | ----------- | ----------- | ----- |
| Yi-VL-6B*[2]     | 65.5% 72.8% | 72.9% 56.2% | 75.5% 41.3% | 55.4% |
LLaVA-InternLM-7B[10] 65.9% 72.6% 68.7% 57.3% 80.0% 37.5% 63.2%
| ShareGPT4V-13B[8]  | 66.7% 75.6% | 73.5% 56.9% | 72.7% 37.0% | 62.4% |
| ------------------ | ----------- | ----------- | ----------- | ----- |
| LLaVA-v1.5-13B[32] | 66.9% 73.1% | 72.4% 60.3% | 75.5% 35.9% | 65.5% |
| Yi-VL-34B*[2]      | 68.4% 72.0% | 78.0% 54.7% | 81.2% 38.6% | 68.2% |
| OmniLMM-12B*[38]   | 69.2% 72.0% | 79.8% 61.0% | 78.0% 40.2% | 66.7% |
| Monkey-Chat[30]    | 69.6% 75.0% | 75.4% 63.3% | 82.4% 46.7% | 58.9% |
InternLM-XComposer*[52] 71.3% 75.7% 76.3% 60.3% 84.5% 44.6% 71.7%
LLaVA-InternLM2-7B[10] 71.6% 78.1% 75.4% 66.7% 77.6% 44.6% 70.2%
LLaVA-InternLM2-20B[10] 72.3% 78.3% 76.6% 68.2% 78.4% 46.2% 69.4%
InternLM-XComposer2*[12] 78.1% 80.4% 83.5% 73.0% 83.7% 63.6% 74.4%
ProprietaryVLMs
| Qwen-VL-Plus[6]  | 64.6% 66.5% | 79.1% 50.2% | 73.9% 42.9% | 57.8% |
| ---------------- | ----------- | ----------- | ----------- | ----- |
| Gemini-Pro-V[44] | 70.2% 70.0% | 78.9% 65.9% | 82.9% 46.2% | 65.9% |
| GPT-4v[37]       | 74.3% 77.6% | 73.8% 71.5% | 85.3% 63.6% | 68.6% |
Qwen-VL-Max[6]
|     | 75.4% 74.8% | 87.2% 67.0% | 85.3% 54.9% | 70.5% |
| --- | ----------- | ----------- | ----------- | ----- |
24

Table12: CircularEvalresultsonMMBench-CN-devset(L-2abilities). Open-sourcemodels
taggedwith*incorporatein-housedatainmodeltraining.
| Model | Overall CP | FP-S FP-C | AR LR | RR  |
| ----- | ---------- | --------- | ----- | --- |
OpenSourceVLMs
| MiniGPT4-13B[56]     | 11.8% 14.6% | 13.8% 14.0% | 15.9% 3.2%  | 2.3%  |
| -------------------- | ----------- | ----------- | ----------- | ----- |
| MiniGPT4-7B[56]      | 11.9% 11.9% | 14.5% 7.8%  | 19.5% 3.2%  | 10.9% |
| OpenFlamingov2[4]    | 14.3% 14.4% | 14.9% 11.2% | 21.3% 10.5% | 12.6% |
| InstructBLIP-13B[11] | 15.1% 16.0% | 14.9% 7.8%  | 30.5% 4.0%  | 14.4% |
| InstructBLIP-7B[11]  | 18.1% 16.0% | 16.6% 10.6% | 38.4% 4.0%  | 23.6% |
IDEFICS-9B-Instruct[26] 18.7% 22.7% 19.7% 7.3% 35.4% 1.6% 17.2%
IDEFICS-80B-Instruct[26] 29.2% 32.0% 27.0% 25.1% 50.0% 8.1% 26.4%
| PandaGPT[43]     | 31.0% 40.1% | 24.9% 18.4% | 47.6% 12.1% | 33.3% |
| ---------------- | ----------- | ----------- | ----------- | ----- |
| VisualGLM-6B[13] | 40.6% 45.3% | 48.1% 30.7% | 54.3% 8.9%  | 37.9% |
CogVLM-Chat-17B[47] 52.9% 63.5% 56.4% 41.9% 65.9% 16.9% 50.0%
| LLaVA-v1.5-7B[32]  | 57.0% 69.3% | 59.9% 47.5% | 62.8% 25.0% | 54.0% |
| ------------------ | ----------- | ----------- | ----------- | ----- |
| Qwen-VL-Chat*[6]   | 57.6% 66.6% | 68.5% 43.6% | 70.1% 21.8% | 48.9% |
| mPLUG-Owl2[50]     | 58.1% 68.8% | 65.1% 43.0% | 68.9% 29.8% | 50.0% |
| ShareGPT4V-7B[8]   | 59.7% 71.8% | 62.6% 48.6% | 62.8% 26.6% | 61.5% |
| OmniLMM-12B*[38]   | 60.6% 67.7% | 69.9% 48.0% | 70.1% 25.8% | 59.2% |
| ShareGPT4V-13B[8]  | 62.4% 72.9% | 67.1% 55.3% | 66.5% 34.7% | 55.7% |
| LLaVA-v1.5-13B[32] | 62.5% 71.8% | 65.7% 57.0% | 67.1% 33.1% | 59.8% |
| MiniCPM-V[39]      | 63.0% 68.2% | 75.1% 53.1% | 72.0% 25.8% | 60.3% |
LLaVA-InternLM-7B[10] 63.0% 72.4% 68.2% 50.3% 68.9% 35.5% 62.1%
| Monkey-Chat[30] | 65.1% 73.8% | 74.4% 50.3% | 77.4% 37.9% | 54.6% |
| --------------- | ----------- | ----------- | ----------- | ----- |
| Yi-VL-6B*[2]    | 65.3% 72.4% | 73.0% 53.1% | 70.7% 33.9% | 67.8% |
| Yi-VL-34B*[2]   | 67.0% 73.8% | 73.0% 52.5% | 72.6% 40.3% | 71.8% |
LLaVA-InternLM2-7B[10] 70.0% 81.5% 72.3% 59.2% 73.8% 34.7% 74.7%
InternLM-XComposer*[52] 71.3% 76.5% 77.5% 63.7% 81.7% 37.9% 71.8%
LLaVA-InternLM2-20B[10] 71.7% 77.9% 74.4% 68.7% 75.6% 43.5% 74.1%
InternLM-XComposer2*[12] 77.2% 83.4% 84.1% 64.2% 84.1% 54.8% 75.9%
ProprietaryVLMs
| Qwen-VL-Plus[6]  | 67.5% 68.8% | 83.0% 54.2% | 75.6% 38.7% | 65.5% |
| ---------------- | ----------- | ----------- | ----------- | ----- |
| Gemini-Pro-V[44] | 69.3% 72.4% | 78.5% 63.1% | 78.7% 40.3% | 65.5% |
| GPT-4v[37]       | 73.3% 76.5% | 71.6% 67.0% | 82.3% 63.7% | 74.1% |
Qwen-VL-Max[6]
|     | 75.9% 73.8% | 85.8% 71.5% | 81.7% 55.6% | 77.0% |
| --- | ----------- | ----------- | ----------- | ----- |
25

Table13: CircularEvalresultsonMMBench-CN-testset(L-2abilities). Open-sourcemodels
taggedwith*incorporatein-housedatainmodeltraining.
| Model | Overall CP | FP-S FP-C | AR LR | RR  |
| ----- | ---------- | --------- | ----- | --- |
OpenSourceVLMs
| MiniGPT4-7B[56]      | 10.8% 9.4%  | 11.8% 5.6% | 24.5% 4.9%  | 8.5%  |
| -------------------- | ----------- | ---------- | ----------- | ----- |
| MiniGPT4-13B[56]     | 13.2% 16.3% | 13.5% 9.0% | 27.3% 3.8%  | 4.3%  |
| OpenFlamingov2[4]    | 13.3% 16.5% | 10.2% 9.0% | 18.8% 11.4% | 12.4% |
| InstructBLIP-13B[11] | 13.7% 13.7% | 14.6% 6.4% | 26.5% 4.3%  | 14.3% |
| InstructBLIP-7B[11]  | 18.1% 15.7% | 18.6% 9.4% | 31.4% 8.7%  | 25.2% |
IDEFICS-9B-Instruct[26] 19.6% 22.4% 17.4% 7.1% 35.9% 6.0% 24.4%
IDEFICS-80B-Instruct[26] 28.8% 33.0% 26.9% 25.1% 41.2% 13.6% 26.0%
| PandaGPT[43]     | 29.6% 40.4% | 20.0% 12.0% | 49.8% 13.0% | 34.1% |
| ---------------- | ----------- | ----------- | ----------- | ----- |
| VisualGLM-6B[13] | 38.1% 44.8% | 39.4% 22.8% | 55.5% 18.5% | 34.9% |
CogVLM-Chat-17B[47] 54.0% 66.1% 49.7% 47.6% 67.8% 26.1% 49.6%
| LLaVA-v1.5-7B[32]  | 56.9% 65.2% | 53.6% 52.1% | 75.5% 31.0% | 50.8% |
| ------------------ | ----------- | ----------- | ----------- | ----- |
| Qwen-VL-Chat*[6]   | 57.5% 63.0% | 64.5% 41.6% | 74.7% 35.9% | 50.0% |
| mPLUG-Owl2[50]     | 58.0% 64.4% | 57.1% 50.2% | 75.1% 31.5% | 56.6% |
| ShareGPT4V-7B[8]   | 58.3% 67.2% | 58.2% 51.3% | 72.7% 28.3% | 54.7% |
| MiniCPM-V[39]      | 59.6% 64.8% | 66.6% 52.8% | 69.0% 33.2% | 54.3% |
| OmniLMM-12B*[38]   | 60.8% 64.8% | 66.4% 53.9% | 74.7% 30.4% | 58.9% |
| LLaVA-v1.5-13B[32] | 62.2% 68.3% | 61.5% 56.9% | 73.5% 35.9% | 64.3% |
| ShareGPT4V-13B[8]  | 62.7% 69.6% | 63.6% 56.2% | 74.7% 36.4% | 60.9% |
| Yi-VL-6B*[2]       | 63.5% 68.7% | 71.7% 52.4% | 74.7% 39.7% | 56.6% |
LLaVA-InternLM-7B[10] 64.1% 70.7% 63.8% 55.8% 75.5% 39.7% 65.5%
| Monkey-Chat[30] | 65.0% 71.5% | 68.9% 52.1% | 80.0% 46.7% | 57.4% |
| --------------- | ----------- | ----------- | ----------- | ----- |
| Yi-VL-34B*[2]   | 66.2% 69.6% | 75.6% 56.2% | 80.0% 37.0% | 61.2% |
InternLM-XComposer*[52] 69.2% 74.8% 71.7% 58.1% 80.8% 39.1% 75.6%
LLaVA-InternLM2-7B[10] 69.9% 75.4% 72.9% 63.7% 81.2% 42.4% 68.6%
LLaVA-InternLM2-20B[10] 70.3% 75.6% 73.5% 67.4% 75.1% 46.2% 69.4%
InternLM-XComposer2*[12] 77.1% 80.4% 82.8% 71.2% 88.2% 55.4% 72.1%
ProprietaryVLMs
| Qwen-VL-Plus[6]  | 67.9% 69.6% | 78.4% 60.3% | 75.1% 48.9% | 61.2% |
| ---------------- | ----------- | ----------- | ----------- | ----- |
| Gemini-Pro-V[44] | 69.2% 68.1% | 77.3% 64.0% | 80.4% 45.7% | 69.8% |
| GPT-4v[37]       | 72.1% 75.0% | 70.1% 70.0% | 82.4% 60.9% | 69.4% |
Qwen-VL-Max[6]
|     | 73.6% 74.4% | 82.6% 69.3% | 79.2% 55.4% | 69.0% |
| --- | ----------- | ----------- | ----------- | ----- |
26

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