Datasets:
File size: 118,003 Bytes
3b138e6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 | | | -Bench: | | Graduate-level | | | Multi-disciplinary | | | Benchmarks | | | for | |
| --- | ------- | --- | -------------- | --- | ------- | ------------------ | --------- | --- | ---------- | --- | --- | --- | --- |
| | | LLM | & MLLM | | Complex | | Reasoning | | Evaluation | | | | |
Meng-HaoGuo1 JiajunXu*1 YiZhang*1 JiaxiSong*1 HaoyangPeng*1 Yi-XuanDeng*1 XinzhiDong*1
KiyohiroNakayama2 ZhengyangGeng3 ChenWang4 BolinNi5 Guo-WeiYang6
YongmingRao†5 HouwenPeng†5 HanHu5 GordonWetzstein2 Shi-MinHu†(cid:66)1
5202 yaM 4 ]VC.sc[ 1v81020.5052:viXra Abstract
Reasoningstandsasacornerstoneofintelligence, 92.3 90.0 M M L U - B e n c h - T
| | | | | | | | | 88.0 | | | | M M M U | - B e n c h - M |
| --- | --- | --- | --- | --- | --- | --- | --- | ---- | --- | --- | --- | ------- | --------------- |
enablingthesynthesisofexistingknowledgeto
78.2
| solve complex | | problems. | Despite | remarkable | | | | | | | | | |
| ------------- | --- | --------- | ------- | ---------- | --- | --- | --- | ---- | --- | --- | --- | ---- | --- |
| | | | | | | | | 69.0 | | | | 69.1 | |
progress,existingreasoningbenchmarksoftenfail
61.2
)%( ycaruccA
| torigorouslyevaluatethenuancedreasoningcapa- | | | | | | | | | 53.6 | | | 53.2 | |
| -------------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | ---- | --- | --- | ---- | --- |
bilitiesrequiredforcomplex,real-worldproblem-
| solving, | particularly | | in multi-disciplinary | | and | | | | | | | | |
| -------- | ------------ | --- | --------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
33.4
| multimodal | contexts. | | In this paper, | | we intro- | | | | | | | | |
| ---------- | --------- | --- | -------------- | --- | --------- | --- | --- | --- | --- | --- | --- | --- | --- |
duceagraduate-level,multi-disciplinary,English-
Chinesebenchmark,dubbedasReasoningBench
| (R-Bench), | for | assessing | the reasoning | | capabil- | | | | | | | | |
| ---------- | --- | --------- | ------------- | --- | -------- | --- | --- | --- | --- | --- | --- | --- | --- |
R-
ityofbothlanguageandmultimodalmodels. o1-20241217 GPT-4o DeepSeek-R1 o1-20241217 GPT-4o
Benchspans1,094questionsacross108subjects
forlanguagemodelevaluationand665questions
| | | | | | | | Figure1.Top-1 | accuracy | comparison | | of different | models | on |
| --- | --- | --- | --- | --- | --- | --- | ------------- | -------- | ---------- | --- | ------------ | ------ | --- |
across 83 subjects for multimodal model test- MMLU,MMMU,andR-Bench. R-Benchposesagreaterchal-
| ing in both | English | | and Chinese. | These | ques- | | lengetocurrentmodels. | | | | | | |
| ----------- | ------- | --- | ------------ | ----- | ----- | --- | --------------------- | --- | --- | --- | --- | --- | --- |
tionsaremeticulouslycuratedtoensurerigorous
difficultycalibration,subjectbalance,andcross-
linguisticalignment,enablingtheassessmentto Reasoning,thesystematicprocessofsynthesizingknowl-
be an Olympiad-level multi-disciplinary bench- edgetosolvenovelproblems,liesattheheartofintelligence.
mark. Weevaluatewidelyusedmodels,includ- Yet,asfoundationmodelsgrowincreasinglysophisticated,
| ingOpenAIo1,GPT-4o,DeepSeek-R1,etc. | | | | | | Ex- | | | | | | | |
| ----------------------------------- | --- | --- | --- | --- | --- | --- | ------------------- | --- | ---- | ------------------ | --- | ------ | ----- |
| | | | | | | | existing benchmarks | | fail | to comprehensively | | assess | their |
perimentalresultsindicatethatadvancedmodels complex reasoning capabilities. As shown in the above
performpoorlyoncomplexreasoning,especially quote,beforeequippingfoundationmodelswithreasoning
multimodalreasoning. Eventhetop-performing skills,weshouldfirstdefinegoalsforthembyestablishing
modelOpenAIo1achievesonly53.2%accuracy areliableevaluationtoassesstheirreasoningcapabilities.
| onourmultimodalevaluation. | | | Dataandcodeare | | | | | | | | | | |
| -------------------------- | --- | --- | -------------- | --- | --- | --- | --------------------------- | --- | --- | --- | ------------------- | --- | --- |
| | | | | | | | Asnotedin(Kahneman,2011)and | | | | (Weietal.,2022),re- | | |
madepubliclyavailableathere.
| | | | | | | | alizing system-I, | | a.k.a., quick | and | intuitive | thinking | and |
| --- | --- | --- | --- | --- | --- | --- | ----------------- | --- | ------------- | --- | --------- | -------- | --- |
system-II,a.k.a.,slowanddeliberatereasoningraisesdis-
| | | | | | | | tinctrequirementsonfoundationmodels. | | | | | Similarly,assess- | |
| --- | --- | --- | --- | --- | --- | --- | ------------------------------------ | --- | --- | --- | --- | ----------------- | --- |
1.Introduction
| | | | | | | | ing quick | thinking | and complex | | reasoning | requires | sub- |
| --- | --- | --- | --- | --- | --- | --- | --------- | -------- | ----------- | --- | --------- | -------- | ---- |
“Settinggoalsisthefirststepinturningtheinvisi- stantiallydifferentassessmentmethods. Ontheonehand,
evaluatingsystem-Ineedstoevaluatetheknowledgeand
| bleintothevisible.” | | | —TonyRobbins | | | | | | | | | | |
| ------------------- | --- | --- | ------------ | --- | --- | --- | ------------- | --- | -------- | ---------- | ------- | ----- | ------- |
| | | | | | | | memory, which | | requires | collecting | various | daily | conver- |
*Secondauthorslistedrandomly,†Jointprojectlead,1Tsinghua
| | | | | | | | sations and | knowledge-based | | questions | | e.g., concept | and |
| --------- | --- | --- | --------- | --- | --- | --- | ----------- | --------------- | --- | --------- | --- | ------------- | --- |
| 2Stanford | | | 3Carnegie | | | | | | | | | | |
University, University, Mellon University, common sense questions. On the other hand, evaluating
4UniversityofPennsylvania,5TencentHunyuanX,6Fitten.Corre-
| | | | | | | | system-IIrequiresevaluatingcomplexreasoningskills. | | | | | | It |
| --- | --- | --- | --- | --- | --- | --- | -------------------------------------------------- | --- | --- | --- | --- | --- | --- |
spondenceto:Shi-MinHu<shimin@tsinghua.edu.cn>.
requiresgatheringadiverserangeofreasoningquestions,
suchasanalyticalanddeductiveones,whichismorechal-
1
R-Bench
| | | | | | | benchmarksasexamples. | | MMLU(Hendrycksetal.,2021) | | |
| ------------ | ------- | ------------------ | --- | --- | --------------- | --------------------- | --- | ------------------------- | --- | --- |
| Table1.Comp. | denotes | comprehensiveness. | | o1 | saturation rep- | | | | | |
isacomprehensivebenchmarkformulti-disciplineunder-
| resentso1(OpenAI,2024b)performanceonthisbenchmark. | | | | | It | | | | | |
| -------------------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
standing,whichhasservedasacriticalguideforthedevelop-
reflectsthechallengethatthebenchmarkposestoadvancedmod-
mentoffoundationmodelsinrecentyears.However,consid-
els.
eringthecurrentlevelofmodelintelligence,thisbenchmark
| Name | | Comp. | o1Saturation | | Language | | | | | |
| ---- | --- | ----- | ------------ | --- | -------- | --- | --- | --- | --- | --- |
isclosetosaturation(e.g.,o1(OpenAI,2024b)hasachieved
| | | ✓ | | | | 92.3%accuracyonit). | | Besides,itdosenottakemultimodal- | | |
| ---- | --- | --- | ----- | --- | --- | ------------------- | --- | -------------------------------- | --- | --- |
| MMLU | | | 0.923 | | en | | | | | |
AIME@2024 ✗ 0.744 en ityandmultilingualismintoconsideration,whichisalsocrit-
R-Bench-T ✓ 0.690 en&zh icalforanidealreasoningtest. MMMU(Yueetal.,2024a)
| | | | | | | isaholisticevaluationformultimodalreasoningtests. | | | | With |
| --- | --- | --- | --- | --- | --- | ------------------------------------------------- | --- | --- | --- | ---- |
✓
| MMMU | | | 0.782 | | en | | | | | |
| ---- | --- | --- | ----- | --- | --- | --- | --- | --- | --- | --- |
thelaunchofo1(OpenAI,2024b),thisbenchmarkisalso
| R-Bench-M | | ✓ | 0.532 | | en&zh | | | | | |
| --------- | --- | --- | ----- | --- | ----- | ----------------------------------------- | --- | --------------------------------- | --- | ------ |
| | | | | | | closetosaturation. | | Also,itcannotbeusedtoevaluatelan- | | |
| | | | | | | guagemodelsandignoresmultilingualtesting. | | | | Weshow |
thecomparisonofMMLU,MMMU,andR-BenchinFig.1
lengingtocollectandfilterthantheformer.Inthispaper,we
| | | | | | | andTab.1. | Frontiermath(Glazeretal.,2024)collectssome | | | |
| --- | --- | --- | --- | --- | --- | --------- | ------------------------------------------ | --- | --- | --- |
focusonbuildingareliablecomplexreasoningbenchmark
| | | | | | | challenging | problems | specifically | designed | for advanced |
| -------------- | -------- | ------ | ------ | --- | ---------- | ------------ | --------- | ------------ | -------- | -------------- |
| for both large | language | models | (LLMs) | and | multimodal | | | | | |
| | | | | | | mathematical | reasoning | evaluation, | which | indicates that |
largelanguagemodels(MLLMs).
currentmodelsstillexhibitweaknessesinmathematicalrea-
Howcanwedesignanidealassessmentforcomplexreason- soning. However,itfallsshortincomprehensivenessand
ing? Webelievefollowingfourpropertiesarecritical. multilingualtesting. ThisalsoappliestoOmni-Math(Gao
| | | | | | | et al., 2024) | and AIME | (OpenAI, | 2024b), | both of which |
| --- | --- | --- | --- | --- | --- | ------------- | -------- | -------- | ------- | ------------- |
• Comprehensiveness. Evaluating the intelligence of serveasbenchmarksfocusedonemployingmathematical
olympiadchallenges.
foundationmodelsisakintoevaluatinghumanintel-
| ligence. | Wecannotfocusonjustoneaspect,suchas | | | | | | | | | |
| -------- | ----------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
Inthispaper,ourgoalistobuildabenchmarkR-Benchthat
mathematics. Acomprehensiveevaluationisessential. alignswiththefourpropertiesweproposedforevaluating
| | | | | | | the reasoning | abilities | of intelligent | models. | To achieve |
| ------------- | --- | ------------ | ---------- | ------ | ------- | ------------- | --------- | -------------- | ------- | ---------- |
| • Difficulty. | | A meaningful | evaluation | should | exhibit | | | | | |
that,wefollowmorethan100collegecoursesfrom19de-
thecapabilitytoeffectivelydiscriminatebetweenthe
performanceofdifferentmodelsandprovidevaluable partmentsatTsinghuaUniversityandcollectchallenging
problemsfromtheirexams,textbooks,quizzes,homework,
| insightsforguidingmodelimprovement. | | | | | Atpresent, | | | | | |
| ----------------------------------- | --- | --- | --- | --- | ---------- | --- | --- | --- | --- | --- |
etc. Aftermultipleroundsofrigorousscreeningbyexperts
foundationmodelsaredevelopingrapidly,andsome
andmodels,wefinallyselect1,094questionsspanning108
simplebenchmarkshavebeensaturatedandcannotpro-
subjectsforlanguagemodelsreasoningtest,and665ques-
videguidanceanddiscriminationforadvancedmodels.
tionscovering83subjectsformultimodalmodelsreasoning
• Multimodality. Weliveinamultimodalworld,con- test. Wewillpresentthedetailedscreeningprocessinthe
stantlyprocessingvariousvisualandlinguisticsignals. Sec2. AfterbuildingtheR-Benchbenchmark,wetestthe
Therefore,anidealbenchmarkshouldbedesignedto reasoningcapabilitiesofvariouspowerfulproprietarymod-
assessbothLLMsandMLLMs. elssuchaso1(OpenAI,2024b),GPT-4o(OpenAI,2024a),
Gemini(Teametal.,2023),Claude(Anthropic,2024a),and
| • Multilingualism. | | We | believe | that performing | com- | | | | | |
| ------------------ | --- | --- | ------- | --------------- | ---- | --- | --- | --- | --- | --- |
open-sourcedmodelssuchasLlama3(Touvronetal.,2023),
plexreasoningismorechallengingthanunderstanding Qwen2.5(Yangetal.,2024),etc. Fromexperiments,our
| multiplelanguages. | | Amodelwithrobustcomplexrea- | | | | | | | | |
| ------------------ | --- | --------------------------- | --- | --- | --- | --- | --- | --- | --- | --- |
observationsandfindingsaresummarizedasfollows:
soningskillsshouldbecapableofsolvingreasoning
| problemsacrossdifferentlanguages. | | | | Thisislikefora | | | | | | |
| --------------------------------- | --- | --- | --- | -------------- | --- | --- | --- | --- | --- | --- |
• Withtheemergenceofadvancedmodelslikeo1,exist-
humanexpert,heorshewillnotlosetheabilitytoad-
ingmultidisciplinaryevaluationshavenearlyreached
| dressproblemsduetolanguagechanges. | | | | | Thus,assess- | | | | | |
| ---------------------------------- | --- | --- | --- | --- | ------------ | ----------- | ------------------------------------ | --- | --- | --- |
| | | | | | | saturation. | Besides,solelyrelyingonmathproblems, | | | |
ingmodelperformanceonequallydifficultquestions
e.g.,mathematicalolympiadproblems,maybringbias
| across | languages | is essential. | It | will provide | insight | | | | | |
| ------ | --------- | ------------- | --- | ------------ | ------- | ------------------ | --- | --------------------------- | --- | --- |
| | | | | | | inmodelevaluation. | | Therefore,thecommunityneeds | | |
intowhetherthemodelhasgenuinelylearnedtoreason
| | | | | | | challenging | | multi-disciplinary | benchmarks | to guide |
| --- | --- | --- | --- | --- | --- | ----------- | --- | ------------------ | ---------- | -------- |
orismerelyoverfittingtoaspecificlanguage.
foundationalmodelsinenhancingtheirreasoningabil-
ities,andthegoalofR-Benchistoaddressit.
Whiletherehavebeenattemptstocreateanidealreasoning
benchmark,tothebestofourknowledge,existingbench- • We illustrate from three dimensions — expert scor-
marks cannot incorporate all four of these key properties ing,modelscoring,andmodelthinkingtime—that
simultaneously. Here,wetakesomewidelyusedreasoning R-Bench is a more complex benchmark with higher
2
R-Bench
requirementsformodelreasoningcomparedtoexisting
multidisciplinarybenchmarksMMLUandMMMU.
Step1:
Step2:Experts collectand
| • Multimodalcomplexreasoningremainschallenging. | | | | | | | Define a list | | | | |
| ----------------------------------------------- | --- | --- | --- | --- | --- | --- | -------------- | --- | --- | --- | --- |
selectreasoningquestions
| Despiterapidadvances,modelslagbehindtext-based | | | | | | | of collected | | | | |
| ---------------------------------------------- | --- | --- | --- | --- | --- | --- | ------------- | --- | --- | --- | --- |
KQ
disciplines
| reasoning. | Forinstance,GPT-4oscores53.6%ontext | | | | | | | | | | |
| ---------- | ----------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
RQ
butonly33.7%inmultimodalreasoningonR-Bench.
| • ChainofThought(CoT)canenhancereasoningabili- | | | | | | Step3: | | | | | |
| ---------------------------------------------- | --- | --- | --- | --- | --- | ------ | --- | --- | --- | ---- | ---- |
| tiesinmostchatmodels,suchasGPT-4o.However,for | | | | | | | | | | Some | Some |
Digitize the questions
| | | | | | | | | | text-only | | multimodal |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | ---------- | --- | ---------- |
reasoningmodelslikeo1-mini,CoTdoesnothavethe
| | | | | | | | | | questions | | questions |
| ----------- | --- | ------------------------------- | --- | --- | --- | --- | --- | --- | --------- | --- | --------- |
| sameeffect. | | Thismaybebecausereasoningmodels | | | | | | | | | |
inherentlybuildCoT,makingexplicitCoTineffective.
• ModelsmaintainhighconsistencyinansweringChi-
neseandEnglishquestionsofequaldifficulty,exceed- Step4:o1 model rescreen
| | | | | | | Step5:The third round | | | based on reasoning difficulty | | |
| --- | --- | --- | --- | --- | --- | ---------------------- | --- | --- | ----------------------------- | --- | --- |
ing70%formostmodels,demonstratingstrongcross-
screensfor completeness,
| lingualreasoningcapabilities. | | | | | | | | | | <2000 | |
| ----------------------------- | --- | --- | --- | --- | --- | ------------- | --------- | --- | --- | ----- | --- |
| | | | | | | repetitionand | ambiguity | | | | |
AQ
| • Foundation | models | perform | differently | across | disci- | | | | | | |
| ------------ | ---------- | ------- | ---------------- | ----------- | ------ | --- | --- | --- | --- | --- | ----- |
| plines. | Specifical | ly , G | P T -4o achieves | 30.4%–68.3% | | | | | | | |
| | | ℛ -B e | nc h- T | | | | | CQ | | | >2000 |
accuracyacrossvariousfields.
2.R-Bench
!-Bench
| | | | | | | | | | Step6: | Constructing | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | ------ | ------------- | --- |
optionsandtranslations
Inthissection,wewillthoroughlyintroducetheconstruc-
| tionprocessofR-Bench. | | | | | | ℛ-Bench | | ℛ-Bench | | | |
| --------------------- | --- | ---------------------------- | --- | --- | --- | ------- | --- | ------- | --- | --- | --- |
| | | Theentireprocessinvolvesmul- | | | | -T | | -T(zh) | | | |
tiplestepssuchasdatacollection,filteringandimproving.
| | | | | | | ℛ-Bench | | ℛ-Bench | | | |
| --- | --- | --- | --- | --- | --- | ------- | --- | ------- | --- | --- | --- |
TheoverallpipelineisillustratedinFig.2.
| | | | | | | -M | | -M(zh) | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | ------ | --- | --- | --- |
2.1.Datacollection
Before gathering reasoning questions, we conducted an Figure2.PipelineofbuildingR-Bench. Theprocessisdivided
intosixsteps,whicharedetailedinSec.2.Thefunnelrepresents
| investigation | of the | curriculum | systems | of graduate | and | | | | | | |
| ------------- | ------ | ---------- | ------- | ----------- | --- | --- | --- | --- | --- | --- | --- |
screening.WealwaysRfiBlteenrcohuttheblueballandpreservethebrown
undergraduatestudentsacross19diffeCrentdepDartmentsat
-TC
TsinghuaUniversity. Basedonoursurvey,weobtaineda one. InStep2,KQandRQdenoteknowledge-basedquestions
| | | | B | | E | andreasoning-basedquestions,respectively. | | | | InStep4,<2000 | |
| --- | --- | --- | --- | --- | --- | ----------------------------------------- | --- | --- | --- | ------------- | --- |
collectionlistcoveringover100coursesacross19depart-
indicatesthatthereasoningtokensofo1arelessthan2000.Finally,
| ments,whichisshowninFig.2Step1. | | | | | | RBench | | RBench | | | |
| ------------------------------- | --- | --- | --- | --- | --- | ------------- | ------------------------------------------- | ------ | --- | --- | --- |
| | | | A | | | inStep5,-AMQE | andCQ-rMeCpresentambiguousquestionsandclear | | | | |
F
After acquiring a collection list, we recruit senior under- questions,respectively. -Tindicatestext-onlytestingforLLMs.
graduatesandgraduatestudentsfromdifferentdepartments -Mmeansmultimodaltesting.zhrepresentstheChineseversion.
| asexpertstoprovidereasoningquestion-answerpairs. | | | | | We | | | | | | |
| ------------------------------------------------ | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
recruitatotalof51experts,withatleasttwoparticipants
| from each | department, | to | help us collect | and filter | ques- | | | | | | |
| ---------------------------------- | ------------- | --- | --------------- | --------------- | --------- | ---------- | --------- | ------ | ---------------- | ------- | ------------ |
| | | | | | | After the | two steps | above, | we collected | a total | of 10,270 |
| tions. Duringthecollectionprocess, | | | | wemainlyfocuson | | | | | | | |
| | | | | | | questions. | Among | them, | 7,163 questions, | | which do not |
| controlling | the following | | key aspects: | 1) The | questions | | | | | | |
includeimages,aredesignatedfortestinglanguagemodels,
should align with the collection list we provide. 2) The whiletheremaining3,107questions,containingimages,are
professionalexpertiseshouldfilterout“knowledge-based”
allocatedfortestingmultimodalmodels.
| questions—those | | that rely | solely on memory | rather | than | | | | | | |
| --------------- | ---------------------------------- | --------- | ---------------- | ------ | -------- | --- | --- | --- | --- | --- | --- |
| reasoning, | suchasconcept-definitionquestions. | | | | Simulta- | | | | | | |
2.2.Datadigitization
| neously, | experts should | retain | reasoning-based | | questions | | | | | | |
| -------- | -------------- | ------ | --------------- | --- | --------- | --- | --- | --- | --- | --- | --- |
andensuretheypresentasufficientdegreeofdifficulty. 3) Afterinitiallycollectingthequestions,wefindthatthecol-
Allquestionsshouldhavecorrespondinganswersthatcan lectedquestionsareinamessyformat,includingpictures,
beautomaticallyverified. Inthiscollectingprocess,weex- screenshots, text, etc. In addition, the summary question
cludeproof-basedquestions,ascurrentautomatedmethods filesprovidedbydifferentexpertsarealsodifferent,includ-
cannotverifythecorrectnessofproofs. Theprocessabove ingpdf,word,excel,etc. Therefore,weneedtoorganize
| isshowninFig.2Step2. | | | | | | anddigitizethisdata. | | | | | |
| -------------------- | --- | --- | --- | --- | --- | -------------------- | --- | --- | --- | --- | --- |
3
R-Bench
Examples for language models reasoning ability test
| | | Major: computerscience; | | | | Major:math; | | | | |
| --- | --- | ----------------------- | --- | --- | --- | ---------------------------------- | --- | --- | --- | --- |
| | | Subject:data structure. | | | | Subject:complex variable function. | | | | |
In the undirected graph G=(V,E) where
V={1,2,3,4,5,6,7} and E={(1,2), (1,3), (2,3), English Chinese
(4,5), (3,6), (4,7), (5,7)}, how many different Consider the polynomial p ( z ) = z 5 + z 3 + 5 z 2 + 2 . p(z)=z5+z3+5z2+2。
| | s p a n n | i n g f o r e s ts d o e s th e gr | a p h G c o n t a in ? | | | | 考虑多项式 | | | |
| --- | ---------- | ------------------------------------------ | ----------------------------- | --------- | ----------------------------- | ----------------------------------- | ----------------------------- | --- | --- | --- |
| | | | | H o w m | a n y z e r o p o in t s ( | c o u n ti n g m u ltiplicities) | 在环域 1<|z|<2 中p 有多少个零点(计入重数)? | | | |
| | N o t e : | I f t h e e d g e s e ts o f t w o | s p a nn in g f o re s ts | do e s p | h a v e i n th e a n n u | l u s 1 < |z | < 2 ? | | | | |
are different, they are considered different
| | spanning forests. | | | A:5 B:3 | | | A:5 B:3 | | | |
| --- | ------------------ | --- | --- | --------- | --- | --- | --------- | --- | --- | --- |
C:2 D:其他答案都不正确
C:2 D:All other answers are incorrect
| | A:14 B:9 C:10 D:12 E:11 | | | E:4 F :6 | | | E:4 F :6 | | | |
| --- | ------------------------------- | --- | --- | ---------- | --- | --- | ---------- | --- | --- | --- |
F:All other answers are incorrect
Examples for multimodal models reasoning ability test
Major:mechanical engineering;
Subject:theoretical mechanics.
| | | English | | | | | Chinese | | | |
| --- | --- | ------- | --- | --- | --- | --- | ------- | --- | --- | --- |
Thewidthofthebrickclampis25cm,and
| | thecurvedrodsAGBandGCEDarehinged | | | | | 砖夹的宽度为 | 25 cm , 曲杆 AGB | | | |
| --- | --------------------------------------- | ----------------------------- | --- | --- | --- | ----------------- | -------------- | --- | --- | --- |
| | | | | | | 与 在 | 点铰接，尺寸如图 | | | |
| | atpointG,withdimensionsasshowninthe | | | | | GCED | G | | | |
| | figure.Supposetheweightofthebrickis | | | | | 所示。设砖重 | Q=120N 提起砖的力 | | | |
| | Q=120NandtheforcePthatliftsthebrick | | | | | P 作用在砖夹的中心线上，砖夹与砖 | | | | |
| | | | | | | 间的摩擦系数 | f=0.5, 若想把砖夹起， | | | |
| | actsalong | thecenterlineofthebrickclamp. | | | | | | | | |
| | Thecoefficientoffrictionbetweenthebrick | | | | | 试求距离b的最大值？ | | | | |
clampandthebrickisf=0.5.Determinethe
A : 9 cm B : 11 cm C : 15 cm
maximumvalueofdistancebrequiredtolift
| | thebrickusingtheclamp. | | | | | D:20 cm E : 25 cm | | | | |
| --- | ---------------------- | --- | --- | --- | --- | ------------------------ | --- | --- | --- | --- |
F: 其他答案都不正确
| | | A : 9 cm B : 11 cm | C : 15 cm | | | | | | | |
| --- | --- | -------------------------- | --------- | --- | --- | --- | --- | --- | --- | --- |
D : 20 cm E : 25 cm F : All other answers are incorrect
Figure3.SomeexamplesinR-Bench.TheseexamplesshowthatR-Benchismultidisciplinary,multimodal,andmultilingual.Asshown
inthefigure,theproblemsinR-Bencharecomplexandcannotbesolvedbyquickthinking,whichshowsthatR-Benchfocusesondeep
reasoningproblemsratherthanknowledgeproblems,suchasconceptualproblems.
| To | do so, | we recruit a data | annotation | team of | about 20 | 2.3.Datafiltering | | | | |
| ------- | ------ | -------------------- | --------------- | ------- | ----------- | ----------------- | --- | --- | --- | --- |
| people. | | They are responsible | for organizing, | | digitizing, | | | | | |
AsshowninFig.2,thefunnelsinsteps2,4,and5represent
checking,andcompilingallthequestionsintoExcelsheets.
threedifferentroundsofdatafiltering.Thesethreeroundsof
Thequestionsusedforlanguagemodelsareorganizedin
screeningrepresentexpertscreening,model-basedfiltering,
thefollowingformat:
andmanualreview.
| “Department | | - Subject - | Question (text) | - Answer | (text) | - | | | | |
| ---------------------------------------------- | --- | ----------- | --------------- | -------- | --------- | --- | --- | --- | --- | --- |
| OriginalQuestion(text,screenshots,photos,etc.) | | | | | -Original | | | | | |
Answer(text,screenshots,photos,etc.)”. Expert-screening. AsmentionedinSec.2.1,werecruit
expertsfromdifferentdepartmentstoprovidequestionsfor
Asforquestionsdesignedformultimodalmodels,theyare
| | | | | | | us. They primarily | rely on their | professional | knowledge | |
| --- | --- | --- | --- | --- | --- | ------------------ | ------------- | ------------ | --------- | --- |
organizedintothefollowingformat:
tofilterout“knowledge-based”questionswhileretaining
“reasoning-based”questions.
| “Department | | - Subject - | Question (text) | - Answer | (text) | - | | | | |
| ----------- | --- | ----------- | --------------- | -------- | ------ | --- | --- | --- | --- | --- |
QuestionImages-OriginalQuestion(text,screenshots,pho-
tosetc.) -OriginalAnswer(text,screenshots,photosetc.)”.
| | | | | | | Model-screening. | OpenAI | o1 (OpenAI, | 2024b) | is a |
| --- | --- | --- | --- | --- | --- | ---------------- | ------ | ----------- | ------ | ---- |
Inthisprocess,weutilizetoolssuchasGPT-4oandMathpix widely used reasoning model. When we call its API, it
| | | | | | | returns the | number of reasoning | tokens, | which, | to some |
| --- | --- | --- | --- | --- | --- | ----------- | ------------------- | ------- | ------ | ------- |
forOCRprocessing,followedbymanualproofreadingto
ensureitiscorrect. Afterthedatateamorganizesthedata, extent,reflectsthedifficultyofthequestion. Inthisround
weperformadouble-checkontheOCRresults. ofscreening,wemainlyfocusonthedifficultyofreasoning.
Wefilteroutthequestionswithlessthan2,000reasoning
tokenstoensurethatourR-Benchisabenchmarkforrea-
soningevaluation.
4
R-Bench
Inorganic Chemistry
Analytical Mechanics
| | | | | | | C h | e m i c a l T h e r m o d y n a m i c s | | | | | | F u n d a m e n t a l P | h ysics | |
| --------------- | --- | --- | ------------ | --- | --- | ----- | ----------------------------------------- | --- | --- | --- | --- | --- | --------------------------- | ------- | --- |
| C h e m i s t | ry | | | | | P h | y s i c a l C h e m i s t r y | | | | | | | | |
| M a t h | | | 3.7%3.7%3.4% | | | | | | | | | | E l e c t r o m a g n e t | i s m | |
4.4% C h e m i c a l R e a c t i o n K i n e t i c s 4.1%3.9%3.8% E l e c t r o d y n a m i c s
| P h y s ic s | | | | 2.9% | | C o | m p u t a t i o n a l C h e m i s t r y | | | | | | | | |
| -------------- | --- | --- | --- | ---- | --- | ----- | ----------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- |
C o m p u t e r S. 4.7% 2.3% O rg a n i c C h e m i s t r y 4.7% 3.8% O p t i c s
| E l e c t ro n | ic E. | | | | 2.3 % | | | | | | 3.5% | | | | |
| ---------------- | ------ | ---- | --- | --- | ----- | ------------------ | ---- | --- | ---- | --- | ---- | ---- | --- | --- | --- |
| | | 4.8% | | | 2 .1% | Quantum Chemistry | | | 4.8% | | | | | | |
| A e r o s pa | c e | | | | | | | | | | | 3.2% | | | |
| Automation | | | | | 2 % | | | | | | | | | | |
| | | | | | 1 .6% | | | 5% | | | | 2.7% | | | |
| Statistics | | 5.4% | | | | | | | | | | 2.6% | | | |
| Mechanical E. | | | | | | | 4.6% | | | | | | | | |
Materials
| | | | | | | | | 5.9% | | | | | 3.8% | | |
| ---------------- | --- | ---- | --- | --- | --- | ----- | ---- | ---- | --- | --- | --- | --- | ------- | --- | --- |
| B i o l o g y | | 7.3% | | | | | | | | | | | | | |
| C i v i l E . | | | | | | 14.7% | 4.4% | | | | | | 9% 1.5% | | |
1.5%
| Vehicle E. | | | | | | | | 6.6% | | | | | | | |
| ----------------- | ------ | ---- | --- | --- | --- | ---- | ---- | ---- | ---- | --- | ----- | --- | ---- | --- | --- |
| Physics E. | | | | | | | 2% | | | | | | 1.4% | | |
| C h e m i ca | l E. | 8.1% | | | | | | | | | | | | | |
| | | | | | | 1.8% | 1% | | | | | | 0.9% | | |
| Ec o n o m | i c s | | | | | | | | | | 10.2% | | | | |
| Microelectronics | | | | | | | 0.5% | | 7.5% | | | | | | |
17.2%
| Environment | | | 9.4% | | | | 0.5% | | | | | | | | |
| ------------- | --- | ------------------------ | ---- | --- | --- | --- | ---- | --- | ------------------------ | --- | ---- | --- | --- | --- | --- |
| Architecture | | | | | | | | | 9.2% | | 9.8% | | | | |
| | | (a)StatisticsofR-Bench-T | | | | | | | (b)StatisticsofR-Bench-M | | | | | | |
Figure4.AccordingtostatisticsonR-Bench,thebenchmarkspans19departments,includingmathematics,physics,biology,computer
science,andchemistry,coveringover100subjectssuchasInorganicChemistry,ChemicalReactionKinetics,andElectromagnetism.It
features1,094questionsdesignedfortestinglanguagemodelsand665questionsspecificallytailoredforevaluatingmultimodalreasoning
capabilities.Foradetailedlistofsubjects,pleaserefertotheappendix.
2.5.OverviewofR-Bench
| Manualreview. | | Ourmanualreviewfocusesonwhether | | | | | | | | | | | | | |
| ------------- | --- | ------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
thequestionconditionsarecomplete,whetherthequestions
| | | | | | | | After | completing | the | aforementioned | | steps, | we develop | | |
| -------------------------------------------------- | --- | --- | --- | --- | --- | --- | -------- | ---------- | --------------- | -------------- | ----------------- | ------ | ------------ | --- | --- |
| arerepeated,whetherthequestionsareambiguous,andthe | | | | | | | R-Bench, | | | | | | | | |
| | | | | | | | | a | graduate-level, | | multi-discipline, | | multilingual | | |
balanceofsubjects.
benchmarkdesignedtoevaluatecomplexreasoningcapa-
Checking for completeness, repetition, and ambiguity re- bilitiesforbothlanguageandmultimodalmodels. Fig.3
quire multiple rounds of thorough review by different in- illustrates several examples from R-Bench, clearly high-
dividuals to eliminate ambiguities, along with the use of lightingitsabovedistinctivefeatures.
| duplicationdetectiontoolstoavoidrepetition. | | | | | | Asforthe | | | | | | | | | |
| ------------------------------------------- | --- | --------- | ------- | --------- | ------- | -------- | ------- | --- | ---------- | --- | --------- | --------------- | --- | --- | --- |
| | | | | | | | R-Bench | can | be divided | | into four | sub-benchmarks: | | R- | 1 |
| balance check, | | to reduce | testing | bias from | subject | imbal- | | | | | | | | | |
Bench-TandR-Bench-T(zh)forlanguagemodelevaluation,
| ance, we | limit | the number | of questions | | per | subject | to a | | | | | | | | |
| -------- | ----- | ---------- | ------------ | --- | --- | ------- | --------- | --- | ------------- | --- | --- | -------------- | --- | ----- | --- |
| | | | | | | | R-Bench-M | | R-Bench-M(zh) | | | | | | |
| | | | | | | | | | and | | | for multimodal | | model | |
maximumof50byfilteringoutexcess.
| | | | | | | | evaluation. | Here,R-Bench-TdenotesR-Benchusingtext- | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | -------------- | -------------------------------------- | ---------- | --- | ------- | ----------- | ------- | --- | --- |
| | | | | | | | only questions | | in English | | for LLM | evaluation, | whereas | | |
2.4.Conductingoptionsandtranslations
R-Bench-T(zh)representsR-Benchusingtext-onlyques-
| | | | | | | | tionsinChineseforLLMevaluation. | | | | | Likewise,theother | | | |
| -------- | --------- | ------- | ----- | --------- | ------------- | --- | ------------------------------- | --- | --- | --- | --- | ----------------- | --- | --- | --- |
| In order | to enable | answers | to be | evaluated | automatically | | | | | | | | | | |
and accurately, we convert all questions such as analyti- twonotationsfollowthesamenamingconvention.
cal,fill-in-the-blank,andmultiple-choicequestionsintothe
WeconductstatisticalanalysisonR-Benchwiththeresults
single-choicequestionformat. WeuseGPT-4otoconstruct ItpresentstheR-Bench-Tstatisticsfor
presentedinFig.4.
| 5 options | for each | question | and | add an | option | “All | other | | | | | | | | |
| --------- | -------- | -------- | --- | ------ | ------ | ---- | ----- | --- | --- | --- | --- | --- | --- | --- | --- |
text-onlyquestionsusedinevaluatingthereasoningcapabil-
answersareincorrect”,whichequipseachquestionwith6 R-Bench-Tspans18departments,
itiesoflanguagemodels.
| candidate | answers. | Then, | we check | the | options | multiple | | | | | | | | | |
| --------------------------------------------- | -------- | ----- | -------- | --- | ------- | -------- | --------------------- | ---------- | ------------ | -------- | ---------- | ------- | -------------- | --- | --- |
| | | | | | | | includingmathematics, | | | biology, | chemistry, | | computersci- | | |
| timestoensurethecorrectnessofourconstruction. | | | | | | Further- | | | | | | | | | |
| | | | | | | | ence, | electronic | engineering, | | and | others. | It encompasses | | |
more,wemanuallyadjusttheoptionstoensureasufficient
over108subjects,suchascalculus,numbertheory,analytic
numericalgapbetweenthem,therebyavoidingerrorscaused
| | | | | | | | geometry, | ordinary | | differential | equations, | | and functional | | |
| --- | --- | --- | --- | --- | --- | --- | --------- | -------- | --- | ------------ | ---------- | --- | -------------- | --- | --- |
bynumericalapproximations. analysis,andcomprisesatotalof1,094questions. Fig.4
alsopresentsthestatisticsofR-Bench-M,whichevaluates
Besides,inordertoenableR-Benchtoacquirethemulti-
| | | | | | | | thereasoningcapabilitiesofmultimodalmodels. | | | | | | R-Bench- | | |
| --- | --- | --- | --- | --- | --- | --- | ------------------------------------------- | --- | --- | --- | --- | --- | -------- | --- | --- |
lingualproperty,wemanuallyconstructedEnglish-Chinese
translationsforeachquestion. Duringthetranslationpro- Mincorporatesadiversesetofquestiontypesrequiringboth
cess,weutilizetoolslikeGPT-4o. Eachquestionismeticu- textualandvisualinputs. Itcovers18departments,suchas
physics,biology,architecture,andeconomics,andincludes
louslyreviewedandrefinedbythreeexpertsfluentinboth
EnglishandChinesetoensurecorrectnessandclarity. 83 subjects, such as thermodynamics, molecular biology,
structuraldesign,andmicroeconomics,includingatotalof
| | | | | | | | 665questions. | | ItisworthnotingthatweprovideEnglish | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | ------------- | --- | ----------------------------------- | --- | --- | --- | --- | --- | --- |
andChineseversionsforallquestions.
5
R-Bench
Table2.Comparisonofreasoningrequirementsforproblemsin Table4.Theaveragethinkingtimeofo1on30randomlyselected
R-Bench-TandMMLUviaexpertando1voting. samplesfromdifferentbenchmarks.TTdenotesthinkingtime.
| | | R-Bench-Twin | | MMLUwin | Tie | | MMLU | | R-Bench-T | MMMU | | R-Bench-M | |
| --- | --- | ------------ | --- | ------- | --- | --- | ---- | --- | --------- | ---- | --- | --------- | --- |
Expertvoting 85.94% 10.62% 3.44% TT 13.5s 98.2s(7.3×) 20.3s 91.7s(4.5×)
| o1voting | | 76.67% | | 20.00% | 3.33% | | | | | | | | |
| -------- | --- | ------ | --- | ------ | ----- | --- | --- | --- | --- | --- | --- | --- | --- |
Table3.Comparisonofreasoningrequirementsforproblemsin
R-Bench-MandMMMUviaexpertando1voting.
3.2.Evaluatingreasoningcapabilityofdifferentmodels
R-Bench-Mwin
| | | | | MMMUwin | Tie | | | | | | | | |
| --- | --- | --- | --- | ------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
WeemployR-Bench-Ttoassessthereasoningcapabilities
| Expertvoting | | 76.88% | | 15.94% | 7.19% | | | | | | | | |
| ------------ | --- | ------ | --- | ------ | ----- | --- | --- | --- | --- | --- | --- | --- | --- |
ofvariousLLMssuchaso1(OpenAI,2024b),GPT-4o(Ope-
| o1voting | | 83.33% | | 13.33% | 3.33% | | | | | | | | |
| -------- | --- | ------ | --- | ------ | ----- | --- | --- | --- | --- | --- | --- | --- | --- |
nAI,2024a),DeepSeek-R1(AI,2025),Gemini(Teametal.,
2024),Claude3.5(Anthropic,2024b),Qwen2.5(Yangetal.,
| | | | | | | 2024), | Llama3 | (Dubey | et | al., 2024), | etc, | in both | English |
| --- | --- | --- | --- | --- | --- | ------------------- | ------ | ------ | --------------------------------- | ----------- | ---- | ------- | ------- |
| | | | | | | andChinesesettings. | | | TheevaluationinvolvesutilizingAPI | | | | |
3.Experiments
| | | | | | | callsanddeployingopen-sourcemodelslocally. | | | | | | | ForAPI |
| --- | --- | --- | --- | --- | --- | ------------------------------------------ | --- | --- | --- | --- | --- | --- | ------ |
calls,weutilizetheofficialinterfaceswithdefaulthyperpa-
AfterdevelopingR-Bench,weutilizeittoassessthecom-
rameters. Foropen-sourcemodels,wedeploytheirweights
plexreasoningcapabilitiesofvariousLLMsandMLLMs,
| | | | | | | locally | usingvLLM | | (Kwon | et al.,2023), | | settingthe | tem- |
| --- | --- | --- | --- | --- | --- | ------- | --------- | --- | ----- | ------------- | --- | ---------- | ---- |
includingbothopen-sourcemodelssuchasLlamaandclose-
sourcemodelssuchasGPT-4o. Firstly,weaimtodemon- perature to 0 while keeping all other parameters at their
stratethatR-Benchisabenchmarkforcomplexreasoning default values. The evaluation was conducted using the
| | | | | | | toolsprovidedbyOpenCompass(Contributors,2023). | | | | | | | In |
| --- | --- | --- | --- | --- | --- | ---------------------------------------------- | --- | --- | --- | --- | --- | --- | --- |
throughexpertscoring(userstudy)ando1modelscoring.
| | | | | | | alltests,theCoTpromptisusedbydefault. | | | | | | Fordetailson | |
| --- | --- | --- | --- | --- | --- | ------------------------------------- | --- | --- | --- | --- | --- | ------------ | --- |
Then,weevaluatethereasoningcapabilitiesofmodelswith
| | | | | | | thespecificprompts, | | | pleaserefertoourappendix. | | | | Inthe |
| --------------------------------------------- | --- | --- | --- | --- | --- | ------------------- | --- | --- | ------------------------- | --- | --- | --- | ----- |
| andwithoutCoTpromptingunderazero-shotsetting. | | | | | Fi- | | | | | | | | |
resultsshowninTab.5,wefoundthatmodelsdesignedfor
nally,weanalyzetheexperimentsandsummarizeobserva-
| | | | | | | reasoning | | tasks, | such as o1, | outperform | | chat models | like |
| --- | --- | --- | --- | --- | --- | --------- | --- | ------ | ----------- | ---------- | --- | ----------- | ---- |
tionsandfindingsfromtheexperimentalprocess.
| | | | | | | GPT-4o | in | complex | reasoning. | Besides, | | there | remains a |
| --- | --- | --- | --- | --- | --- | ------ | --- | ------- | ---------- | -------- | --- | ----- | --------- |
significantgapincomplexreasoningbetweenopen-source
3.1.Reasoningcomparisonwithotherbenchmarks
modelsandcommercialmodels.
ToillustratethatR-Benchisabenchmarkdesignedtoeval-
R-Bench-M
| | | | | | | Moreover, | | we utilize | | | to evaluate | the | reason- |
| -------------- | -------- | --- | --------- | ------------ | ------ | --------- | --- | ---------- | --- | --- | ----------- | --- | ------- |
| uate reasoning | ability, | | we employ | two methods: | expert | | | | | | | | |
ingcapabilitiesofvariousMLLMs,includingo1(OpenAI,
scoringandreasoningmodelscoring.
2024b),GPT-4o(OpenAI,2024a),Claude3.5(Anthropic,
Weconductedexpertscoringthroughuserstudies.Tobespe- 2024b), Qwen2.5-VL (Yang et al., 2024), and InternVL
cific,werandomlyselected30questionsfromR-Bench-T 2.5(Chenetal.,2024),etc,acrossbothEnglishandChinese
andanother30questionsfromMMLUandpresentedthem languages. TheevaluationalsoinvolvesutilizingAPIcalls
to experts for pairwise comparisons to determine which anddeployingopen-sourcemodelslocally. ForAPIcalls,
questionrequiredmorereasoningskillstosolve. Wecon- weutilizetheofficialinterfaceswithdefaulthyperparam-
structed similar experiments using the same settings be- eters. For open-source models, we deploy their weights
tweenR-Bench-MandMMMU. locallyusingVLMEvalKit(Duanetal.,2024),settingthe
temperatureto0whilekeepingallotherparametersattheir
Forreasoningmodelscoring,weadoptedtwoapproaches.
| | | | | | | defaultvalues. | | Inalltests,theCoTpromptisusedbyde- | | | | | |
| ------------ | --- | ---- | ------ | ------------------ | ----- | -------------- | --- | ---------------------------------- | --- | --- | --- | --- | --- |
| On one hand, | we | used | the o1 | model to determine | which | | | | | | | | |
fault. Fordetailsonthespecificprompts,pleaserefertoour
questionrequiredmorereasoningabilitybasedonthenum-
| | | | | | | appendix. | | We draw | three | conclusions | from | the | results in |
| --- | --- | --- | --- | --- | --- | --------- | --- | ------- | ----- | ----------- | ---- | --- | ---------- |
berofreasoningtokens(reasoningtime);ontheotherhand,
Tab.6. First,wefoundthatmodelsperformworseinmulti-
weaskedtheo1modeltodirectlycomparethetwoquestions
modalcomplexreasoningcomparedtoreasoninginapurely
anddeterminewhichonerequiredmorereasoning.
| | | | | | | linguistic | | environment. | Second, | | the reasoning | | model o1 |
| --- | --- | --- | --- | --- | --- | ---------- | --- | ------------ | ------- | --- | ------------- | --- | -------- |
TheresultsareshowninTab.2,3and4. Theresultsindicate stilldemonstratesoutstandingperformanceinmultimodal
thatbotho1’sjudgmentandtheexperts’judgmentconsider complexreasoningevaluation. Third,thegapbetweenopen-
R-Bench to require significantly higher reasoning ability sourceandclosed-sourcemodelsisevenmorepronounced
| comparedtoMMLUandMMMU. | | | | | | inmultimodalcomplexreasoning. | | | | | | | |
| ---------------------- | --- | --- | --- | --- | --- | ----------------------------- | --- | --- | --- | --- | --- | --- | --- |
6
R-Bench
Table5.PerformancecomparisonofvariousmodelsonR-Bench- Table6.PerformancecomparisonofvariousmodelsonR-Bench-
Tinzero-shotsettingswithCoT.Thetableisdividedbyamiddle Minzero-shotsettingswithCoT.Thetableisdividedbyamiddle
line:API-basedmodelsarelistedabovetheline,whileopen-source line:API-basedmodelsarelistedabovetheline,whileopen-source
modelsareshownbelow.‘zh’indicatestheChineseversion.The modelsareshownbelow.’zh’indicatestheChineseversion.The
valuesinthetablerepresenttheTop-1accuracy,in%. valuesinthetablerepresenttheTop-1accuracy,in%.
| | R-Bench-T | R-Bench-T(zh) | | | R-Bench-M | R-Bench-M(zh) |
| ----------- | --------- | ------------- | ----------- | --- | --------- | ------------- |
| ModelName | | | ModelName | | | |
| o1-20241217 | 69.0 | 70.1 | o1-20241217 | | 53.2 | 55.0 |
Gemini-2.0-flash-thinking 68.4 67.5 Claude3.5-sonnet@1022 39.7 38.3
Doubao1.5pro-20250121 62.0 63.4 Doubao1.5pro-20250121 37.9 42.4
| o1-preview@20240912 | 62.3 | 62.6 | GPT-4o-20241120 | | 33.4 | 33.2 |
| --------------------- | ---- | ---- | ---------------------- | --- | ---- | ---- |
| o1-mini@20240912 | 64.0 | 59.9 | Gemini-1.5-Pro | | 35.5 | 35.9 |
| Doubao-pro-20241215 | 60.7 | 60.8 | | | | |
| | | | Qwen2-VL-72B | | 25.1 | 25.7 |
| Claude3.5-sonnet@0620 | 57.5 | 57.0 | | | | |
| | | | Qwen2-VL-7B | | 19.6 | 22.3 |
| GPT-4o-20241120 | 53.6 | 51.6 | | | | |
| | | | LLaVA-OneVision-7B | | 23.8 | 23.5 |
| MiniMax-Text-01 | 53.8 | 53.6 | | | | |
| | | | DeepSeek-VL2 | | 21.8 | 24.4 |
| GLM-Zero-Preview | 53.6 | 48.6 | | | | |
| | | | Llama3.2V-11B-Instruct | | 20.0 | 18.6 |
| ERNIE-4.0-8K-Latest | 39.7 | 50.1 | | | | |
| | | | InternVL-2.5-8B | | 15.9 | 17.1 |
| Deepseek-R1 | 61.2 | 59.3 | | | | |
| Deepseek-V3 | 59.6 | 56.6 | | | | |
| Qwen3-235B-A22B | 58.0 | 58.4 | | | | |
Table7.AssessingtheperformanceimpactofCoTacrossdifferent
| Qwen3-32B | 52.3 | 54.3 | modelsonR-Bench-T. | | | |
| ---------------------- | ---- | ---- | --------------------- | --- | ------- | --------- |
| Qwen2.5-72B-Instruct | 53.7 | 52.0 | | | | |
| Llama-3.3-70B-Instruct | 49.5 | 47.6 | ModelName | | wCoT(%) | w/oCoT(%) |
| Qwen2.5-32B-Instruct | 50.8 | 49.9 | | | | |
| | | | o1-mini@20240912 | | 64.0 | 64.0 |
| Gemma-2-27b-it | 36.0 | 38.9 | | | | |
| | | | GPT-4o-20241120 | | 53.6 | 51.5 |
| Phi-4-14B | 55.3 | 47.3 | LLAMA3.3-70B-Instruct | | 49.5 | 47.4 |
| Phi-3-14B | 29.5 | 24.4 | | | | |
| | | | Qwen2.5-32B-Instruct | | 50.8 | 44.6 |
| Qwen3-8B | 47.5 | 45.9 | | | | |
| | | | Qwen2.5-7B-Instruct | | 43.6 | 42.6 |
| InternLM3-8B-Instruct | 41.1 | 45.8 | | | | |
| Qwen2.5-7B-Instruct | 43.6 | 44.5 | | | | |
GLM-4-9b-chat 25.6 32.4 focused modelssuch as o1-mini. We conjecture thatthis
| Llama-3.1-8B-Instruct | 26.1 | 23.6 | | | | |
| --------------------- | ---- | ---- | --- | --- | --- | --- |
discrepancyarisesbecausereasoningmodelsinherentlyuti-
| Llama-3.2-3B-Instruct | 24.2 | 24.0 | | | | |
| --------------------- | ---- | ---- | --- | --- | --- | --- |
lizeCoT-likemechanisms,leadingtotheexplicitaddition
ofCoTredundantandineffective.
3.3.Observationsandfindings
| | | | Consistency | between | English and | Chinese questions. |
| --- | --- | --- | ----------- | ------- | ----------- | ------------------ |
Multimodalreasoningremainschallengingforcurrent AsshowninFig.5,wetestedtheconsistencyofthesame
questionacrossdifferentlanguagesonR-Bench-T.Itcanbe
models. Wecomparedtheperformanceofthesamemodel
onR-Bench-TandR-Bench-M.Forexample,o1achieved
observedthatmostmodels,suchaso1,Doubao1.5pro,and
69.0%onR-Bench-Tbutonly53.2%onR-Bench-M.The GPT-4o,exhibitacertaindegreeofconsistencyacrossdif-
samesituationalsooccursinothermodels,suchasGPT-4o. ferentlanguages.Thissuggeststhatfoundationmodelshave
Itindicatesthatthemodel’scapabilityinlanguagereasoning alreadydemonstratedacertainlevelofintelligence,enabling
significantlysurpassesitsabilityinmultimodalreasoning. themtoperformreasoningonproblemsofthesamediffi-
Therefore,akeyfocusofresearchintherecentfuturewill cultyindifferentlinguisticenvironments. However,these
behowtotransferlinguisticintelligencetothemultimodal modelsarenotperfectandstillrequirefurtherimprovement
domain. inthisaspect. Thisconsistencyreflectstheextenttowhich
| | | | themodeloverfitsdifferentlanguages. | | | Therefore,wehope |
| --- | --- | --- | ----------------------------------- | --- | --- | ---------------- |
futuremodelswillfocusmoreonlearninghowtoreason
| TheeffectofCoT. InTab.7,wetestedtheeffectofCoT | | | | | | |
| ---------------------------------------------- | --- | --- | --- | --- | --- | --- |
onfivemodels. Asseeninthetable,mostmodelsbenefit ratherthanmerelyfittingtospecificlanguages.
| fromCoT,butithasnoimpactono1-mini. | | Theresultsindi- | | | | |
| ---------------------------------- | --- | --------------- | --- | --- | --- | --- |
catethatCoTenhancestheperformanceofchatmodelslike Modelsshowsignificantperformancevariationacross
GPT-4o. However,ithasnonotableimpactonreasoning- disciplines. Fig.6showstheperformanceofGPT-4oin
7
R-Bench
leveragedacrossdiversefieldssuchaswriting,coding,edu-
cation,healthcare,finance,andmore,servingasasource
Consistency of English and Chinese questions with the same difficulty
86.7%
| | | | | | | forprovidingintelligence. | | | Now, | foundationmodelshave | | | |
| --- | -------- | --- | --- | --- | --- | ------------------------- | --- | --- | ---- | -------------------- | --- | --- | --- |
| | 80 78.5% | | | | | | | | | | | | |
75.2% 73.5% 73.4% 73.3% becomeanessentialpartofourdailyworkandlife.
| | | | 72.4% 70.5% | 69.3% 69.2% | | | | | | | | | |
| --- | --- | --- | ----------- | ----------- | ----------------- | ------------------------------------------------- | --- | --- | --- | --- | --- | --- | --- |
| | | | | | 68.3% 68.0% 66.9% | | | | | | | | |
| | 60 | | | | | Tobuildahigh-qualityfoundationmodel,webelievethat | | | | | | | |
ycnetsisnoC fivekeyaspectsareessential: pre-training(Vaswanietal.,
2017;Raffeletal.,2020;Sunetal.,2023;Radfordetal.,
40
2021;2018),supervisedfine-tuning(Ouyangetal.,2022;
| | | | | | | Liu et al., | 2024b; | Pareja | et | al., 2024; | Li | et al., | 2024; |
| --- | --- | --- | --- | --- | --- | ----------- | ------ | ------ | --- | ---------- | --- | ------- | ----- |
20
| | | | | | | Zhu et al., | 2023; | Taori | et al., | 2023), | preference | optimiza- | |
| --- | --- | --- | --- | --- | --- | ----------- | ----- | ----- | ------- | ------ | ---------- | --------- | --- |
tion(Rafailovetal.,2024;Schulmanetal.,2017;Lightman
| | 0 7 5 | 2 c t 0 | 0 c t i t | c t uc t -8B-Instruct | B .5-7B-Instruct | | | | | | | | |
| --- | ----- | ------- | --------- | --------------------- | ---------------- | --- | --- | --- | --- | --- | --- | --- | --- |
4 1 2 1 4 1 2 1 4 0 9 1 s t r u onnet@ 0 6 2 4 1 1 2 -32B-Inst ru -2 7 b - s tr u s tr 4 -1 4 et al., 2023; Pal et al., 2024; Azar et al., 2024),test-time
| | 20 2 -2 0 2 @2 | 0 2 B -I n | 20 2 a -2 70B - In | B - In | Ph i- | | | | | | | | |
| --- | -------------- | ---------- | ------------------ | ------- | ----- | --- | --- | --- | --- | --- | --- | --- | --- |
| | o1- -p ro w | -7 2 s 4o- | m m - | .1 -8 3 | | | | | | | | | |
a o e v ie n 2 . 5 3 . 5 - G P T - n 2 . 5 G e a- 3 . 3 a - 3 rn L M e n 2 enhancement(Brownetal.,2020;Weietal.,2022;OpenAI,
| | D o u b 1 -p r | Q w e au d e | Q w e la m L la m | nt e | Q w | | | | | | | | |
| --- | -------------- | ------------ | ----------------- | ---- | --- | ------------------------------------------------ | --- | ------- | ------- | ----- | ---------- | ----- | ---- |
| | o | C l | L | I | | 2024b;DeepSeek,2024;Dongetal.,2022),andtrustwor- | | | | | | | |
| | | | | | | thy evaluation | | (Chiang | et al., | 2024; | Li et al., | 2023; | Chen |
Figure5.Theperformanceofdifferentmodelsonquestionsofthe
| | | | | | | et al., 2021; | Jain | et al., | 2024; | Yu et | al., 2023). | Reliable | |
| --- | --- | --- | --- | --- | --- | ------------- | ---- | ------- | ----- | ----- | ----------- | -------- | --- |
samedifficultyinChineseandEnglish.
evaluationplaysacrucialroleinrevealingmodels’weak-
nessesandshortcomings,guidingfurtheroptimizationand
improvement,whichisalsothefocusofthispaper.
Accuracy of Different Departments
70 68.3%68.0% Average: 53.6% 4.2.Evaluationforfoundationmodels
62.5%
| | | 61.0% | | | | Evaluatingtheintelligenceoffoundationmodelsisamul- | | | | | | | |
| --- | --- | ----- | --- | --- | --- | -------------------------------------------------- | --- | --- | --- | --- | --- | --- | --- |
| | 60 | 58.4% | | | | | | | | | | | |
55.9%55.8%55.3%54.5%54.2% tifacetedandcomplexchallenge,akintoassessinghuman
53.6%
)%( ycaruccA 51.0% intelligence. Researchers have introduced various evalu-
| | 50 | | 48.0% | | | | | | | | | | |
| --- | --- | --- | ----- | --- | --- | ----------------- | --- | ------- | ----------- | --- | ---- | ------------- | --- |
| | | | | | | ation benchmarks, | | broadly | categorized | | into | fast-thinking | |
44.9%
| | | | | | | assessments | ( | a.k.a., system-I | | evaluation | | (Chiang | et al., |
| --- | --- | --- | --- | ---------- | --- | ------------ | --- | ---------------- | ----- | ---------- | ---------- | ------- | ------- |
| | 40 | | | 39.0%38.9% | | | | | | | | | |
| | | | | | | 2024; Dubois | | et al., 2024; | Zheng | et | al., 2023; | Lin | et al., |
34.4%33.3%
2021;2024;Luetal.,2022;Yuetal.,2023;Lietal.,2023),
30.4%
30
whichrequiresthefoundationmodelstomemorizeexten-
siveknowledgeandretrieveefficiently,andslow-thinking
| | Biolog y s E . tronic E | . erospac e hemistry Ma th utomation | s ic s on ic s hanical E. Statist ic s emica l E . | puter S. Materials Civil E | . e E . ironme nt conomics | | | | | | | | |
| --- | ------------------------ | ------------------------------------ | -------------------------------------------------- | -------------------------- | --------------------------- | --- | --- | --- | --- | --- | --- | --- | --- |
hysic Ph y lectr ehicl assessments(a.k.a.,system-IIevaluation(Hendrycksetal.,
| | P E l ec | A C A | o e e c C h C om | | V E n v E | | | | | | | | |
| --- | -------- | ----- | ---------------- | --- | --------- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | | M i cr M | | | | | | | | | | |
2021;Yueetal.,2024a;Luetal.,2023;Wangetal.,2024a;
Liuetal.,2024c;Chenetal.,2021;Jimenezetal.,2023),
R-Bench-T
| Figure6.GPT-4o | | on | across | different | departments, | | | | | | | | |
| -------------- | --- | --- | ------ | --------- | ------------ | --- | --- | --- | --- | --- | --- | --- | --- |
whichemphasizesthecomplexreasoningskillsoffounda-
whichshowslargevariationamongdifferentdisciplines.. R-Benchfocusesonthelatter.
tionmodels.
MMLU(Hendrycksetal.,2021)isthepioneerinreasoning
different areas of the R-Bench-T benchmark. From the evaluation,whichproposesamulti-disciplineunderstanding
test. Afterthat,lotsofmulti-disciplinebenchmarks(Rein
figure,itcanbeobservedthattheperformancevariessig-
nificantlyacrossdifferentdomains,witharangereaching etal.,2023;Wangetal.,2024b;Yueetal.,2024b)atthe
37.9%. This suggests that if we want to improve the rea- undergraduateorgraduatelevelareproposedforreasoning
| | | | | | | assessment. | However,withtherapiddevelopmentofintelli- | | | | | | |
| --- | --- | --- | --- | --- | --- | ----------- | ----------------------------------------- | --- | --- | --- | --- | --- | --- |
soningabilityofmodels,weshouldtakeacomprehensive
approachratherthanfocusingsolelyonimprovementsina gentmodels(OpenAI,2024b),thesebenchmarksareclose
| | | | | | | tosaturation. | Besides,severalstudies(Glazeretal.,2024; | | | | | | |
| --- | --- | --- | --- | --- | --- | ------------- | ---------------------------------------- | --- | --- | --- | --- | --- | --- |
singlesubject,suchasmathematics.
Gaoetal.,2024;Luetal.,2023;Wangetal.,2024a)assess
reasoningabilitythroughcomplexmathematicalproblems
4.RelatedWork
| | | | | | | such as | mathematical | | olympiad | challenges, | | which | bring |
| --- | --- | --- | --- | --- | --- | ------- | ------------ | --- | -------- | ----------- | --- | ----- | ----- |
4.1.Foundationmodels challengesandguidancetocurrentfoundationmodels.How-
ever,onlyguidingthemodeltoimproveitsmathematical
WiththeemergenceofChatGPT(OpenAI,2022),founda-
| | | | | | | reasoning | skills | appears | to be | limited. | In this | paper, | our |
| --- | --- | --- | --- | --- | --- | --------- | ------ | ------- | ----- | -------- | ------- | ------ | --- |
tionmodels(Ouyangetal.,2022;Touvronetal.,2023;Yang
targetistobuildareliablebenchmarkforLLMandMLLM
etal.,2024;Jiangetal.,2023;Teametal.,2024;Zengetal., reasoning evaluation, which matches the comprehensive-
| 2022; | Bi et | al., 2024; | Liu et al., 2024a; | Cai | et al., 2024; | | | | | | | | |
| ----- | ----- | ---------- | ------------------ | --- | ------------- | --- | --- | --- | --- | --- | --- | --- | --- |
nessofMMLU(Hendrycksetal.,2021)whileachieving
Anthropic,2024a;Wuetal.,2024)areincreasinglybeing
8
R-Bench
thedifficultyofmathematicalolympiadquestions. Z., Wei, X., Weng, Q., Wu, F., Xiong, Y., Xu, C., Xu,
R.,Yan,H.,Yan,Y.,Yang,X.,Ye,H.,Ying,H.,Yu,J.,
| 5.Conclusion | | | | | | Yu,J.,Zang,Y.,Zhang,C.,Zhang,L.,Zhang,P.,Zhang, | | | | | | |
| ------------ | --- | --- | --- | --- | --- | ----------------------------------------------- | --- | --- | --- | --- | --- | --- |
P.,Zhang,R.,Zhang,S.,Zhang,S.,Zhang,W.,Zhang,
Inthispaper,weproposedR-Bench,agraduate-levelmulti- W.,Zhang,X.,Zhang,X.,Zhao,H.,Zhao,Q.,Zhao,X.,
disciplinary, multilingual benchmark for both LLM and Zhou,F.,Zhou,Z.,Zhuo,J.,Zou,Y.,Qiu,X.,Qiao,Y.,
MLLMreasoningevaluation,whichhascoveragesimilar andLin,D. Internlm2technicalreport,2024.
| to MMLU | and MMMU | while reaching | | the difficulty | of | | | | | | | |
| ------- | -------- | -------------- | --- | -------------- | --- | --- | --- | --- | --- | --- | --- | --- |
Chen,M.,Tworek,J.,Jun,H.,Yuan,Q.,Pinto,H.P.D.O.,
| mathematicalcompetitionssuchasAIME@2024. | | | | Weeval- | | | | | | | | |
| ---------------------------------------- | --- | --- | --- | ------- | --- | --- | --- | --- | --- | --- | --- | --- |
Kaplan,J.,Edwards,H.,Burda,Y.,Joseph,N.,Brockman,
uatedmultipleclosed-sourceandopen-sourcemodelssuch
| | | | | R-Bench | | G., etal. | Evaluatinglargelanguagemodelstrainedon | | | | | |
| --------- | ----------- | ------------ | --- | ------- | --- | --------- | -------------------------------------- | --- | --- | --- | --- | --- |
| as OpenAI | o1, GPT-4o, | DeepSekk-R1, | | etc, on | | | | | | | | |
code. arXivpreprintarXiv:2107.03374,2021.
andobservedboththeprogressandlimitationsofcurrent
modelsinreasoning. Later,wewillmakethedataandcode Chen, Z., Wang, W., Cao, Y., Liu, Y., Gao, Z., Cui, E.,
| available, | hoping to provide | guidance | and | insight | for the | | | | | | | |
| ---------- | ----------------- | -------- | --- | ------- | ------- | -------- | ------- | ----- | -------- | --------- | ------------- | --- |
| | | | | | | Zhu, J., | Ye, S., | Tian, | H., Liu, | Z., etal. | Expandingper- | |
developmentoffoundationmodels.
formanceboundariesofopen-sourcemultimodalmodels
| | | | | | | withmodel,data,andtest-timescaling. | | | | | arXivpreprint | |
| ---------- | --- | --- | --- | --- | --- | ----------------------------------- | --- | --- | --- | --- | ------------- | --- |
| References | | | | | | arXiv:2412.05271,2024. | | | | | | |
AI, D. Deepseek-r1: Incentivizing reasoning capabil- Chiang,W.-L.,Zheng,L.,Sheng,Y.,Angelopoulos,A.N.,
ity in llms via reinforcement learning. arXiv preprint Li, T., Li, D., Zhang, H., Zhu, B., Jordan, M., Gonza-
arXiv:2501.12948,2025. lez, J. E., et al. Chatbot arena: An open platform for
| | | | | | | evaluating | llms | by human | | preference. | arXiv | preprint |
| --- | --- | --- | --- | --- | --- | ---------- | ---- | -------- | --- | ----------- | ----- | -------- |
Anthropic. Theclaude3modelfamily: Opus,sonnet,haiku. arXiv:2403.04132,2024.
https://www.anthropic.com/news/claude-3-family,2024a.
| | | | | | | Contributors, | O. | Opencompass: | | A universal | | evaluation |
| ---------- | ----------- | --- | ------ | --- | ------- | ------------- | -------------- | ------------ | ------- | --------------- | --- | ---------- |
| Anthropic. | Introducing | | claude | 3.5 | sonnet. | | | | | https://github. | | |
| | | | | | | platform | for foundation | | models. | | | |
https://www.anthropic.com/news/claude-3-5-sonnet, com/open-compass/opencompass,2023.
2024b.
| | | | | | | DeepSeek. | | Deepseek-r1 | | release. | | https://api- |
| --- | --- | --- | --- | --- | --- | --------- | --- | ----------- | --- | -------- | --- | ------------ |
Azar,M.G.,Guo,Z.D.,Piot,B.,Munos,R.,Rowland,M., docs.deepseek.com/news/news250120,2024.
| Valko, | M., and Calandriello, | D. | A general | theoretical | | | | | | | | |
| ------ | --------------------- | --- | --------- | ----------- | --- | --------- | ------- | ---- | ---------- | --- | ------- | ------------ |
| | | | | | | Dong, Q., | Li, L., | Dai, | D., Zheng, | C., | Ma, J., | Li, R., Xia, |
paradigmtounderstandlearningfromhumanpreferences.
InInternationalConferenceonArtificialIntelligenceand H.,Xu,J.,Wu,Z.,Liu,T.,etal. Asurveyonin-context
| | | | | | | learning. | arXivpreprintarXiv:2301.00234,2022. | | | | | |
| --- | --- | --- | --- | --- | --- | --------- | ----------------------------------- | --- | --- | --- | --- | --- |
Statistics,pp.4447–4455.PMLR,2024.
| | | | | | | Duan, H., | Yang, | J., Qiao, | Y., | Fang, | X., Chen, | L., Liu, |
| --------------------------------- | --------- | --------- | -------- | ------------ | --- | ----------- | -------------------------------------- | --------- | ---------- | ----- | --------- | ---------- |
| Bi, X., Chen, | D., Chen, | G., Chen, | S., Dai, | D., Deng, | C., | | | | | | | |
| | | | | | | Y., Dong, | X., | Zang, | Y., Zhang, | | P., Wang, | J., et al. |
| Ding,H.,Dong,K.,Du,Q.,Fu,Z.,etal. | | | | Deepseekllm: | | | | | | | | |
| | | | | | | Vlmevalkit: | Anopen-sourcetoolkitforevaluatinglarge | | | | | |
Scalingopen-sourcelanguagemodelswithlongtermism.
| | | | | | | multi-modality | | models. | In | Proceedings | | of the 32nd |
| --- | --- | --- | --- | --- | --- | -------------- | --- | ------- | --- | ----------- | --- | ----------- |
arXivpreprintarXiv:2401.02954,2024.
ACMinternationalconferenceonmultimedia,pp.11198–
| Brown,T.,Mann,B.,Ryder,N.,Subbiah,M.,Kaplan,J.D., | | | | | | 11201,2024. | | | | | | |
| ------------------------------------------------- | ---------------------------------- | --- | ------ | ----------- | --- | ----------- | ------- | --- | ------- | ----------- | --- | --------- |
| Dhariwal, | P., Neelakantan, | A., | Shyam, | P., Sastry, | G., | | | | | | | |
| | | | | | | Dubey, A., | Jauhri, | A., | Pandey, | A., Kadian, | A., | Al-Dahle, |
| Askell,A.,etal. | Languagemodelsarefew-shotlearners. | | | | | | | | | | | |
A.,Letman,A.,Mathur,A.,Schelten,A.,Yang,A.,Fan,
Advancesinneuralinformationprocessingsystems,33:
| | | | | | | A., et al. | The | llama | 3 herd | of models. | arXiv | preprint |
| --- | --- | --- | --- | --- | --- | ---------- | --- | ----- | ------ | ---------- | ----- | -------- |
1877–1901,2020.
arXiv:2407.21783,2024.
Cai,Z.,Cao,M.,Chen,H.,Chen,K.,Chen,K.,Chen,X.,
Dubois,Y.,Galambosi,B.,Liang,P.,andHashimoto,T.B.
Chen,X.,Chen,Z.,Chen,Z.,Chu,P.,Dong,X.,Duan,
| | | | | | | Length-controlled | | alpacaeval: | | A simple | way | to debias |
| --- | --- | --- | --- | --- | --- | ----------------- | --- | ----------- | --- | -------- | --- | --------- |
H.,Fan,Q.,Fei,Z.,Gao,Y.,Ge,J.,Gu,C.,Gu,Y.,Gui,
| | | | | | | automaticevaluators. | | | arXivpreprintarXiv:2404.04475, | | | |
| --- | --- | --- | --- | --- | --- | -------------------- | --- | --- | ------------------------------ | --- | --- | --- |
T.,Guo,A.,Guo,Q.,He,C.,Hu,Y.,Huang,T.,Jiang,T.,
2024.
Jiao,P.,Jin,Z.,Lei,Z.,Li,J.,Li,J.,Li,L.,Li,S.,Li,W.,
Li,Y.,Liu,H.,Liu,J.,Hong,J.,Liu,K.,Liu,K.,Liu,X., Gao, B., Song, F., Yang, Z., Cai, Z., Miao, Y., Dong, Q.,
Lv,C.,Lv,H.,Lv,K.,Ma,L.,Ma,R.,Ma,Z.,Ning,W., Li, L., Ma, C., Chen, L., Xu, R., et al. Omni-math:
Ouyang,L.,Qiu,J.,Qu,Y.,Shang,F.,Shao,Y.,Song,D., A universal olympiad level mathematic benchmark for
Song,Z.,Sui,Z.,Sun,P.,Sun,Y.,Tang,H.,Wang,B., largelanguagemodels. arXivpreprintarXiv:2410.07985,
| Wang,G.,Wang,J.,Wang,J.,Wang,R.,Wang,Y.,Wang, | | | | | | 2024. | | | | | | |
| --------------------------------------------- | --- | --- | --- | --- | --- | ----- | --- | --- | --- | --- | --- | --- |
9
R-Bench
Glazer,E.,Erdil,E.,Besiroglu,T.,Chicharro,D.,Chen,E., Lin, S., Hilton, J., and Evans, O. Truthfulqa: Measuring
Gunning,A.,Olsson,C.F.,Denain,J.-S.,Ho,A.,Santos, how models mimic human falsehoods. arXiv preprint
E.d.O.,etal. Frontiermath: Abenchmarkforevaluating arXiv:2109.07958,2021.
| advancedmathematicalreasoninginai. | | | arXivpreprint | | | | | |
| ---------------------------------- | --- | --- | ------------- | --- | --- | --- | --- | --- |
arXiv:2411.04872,2024. Liu,A.,Feng,B.,Wang,B.,Wang,B.,Liu,B.,Zhao,C.,
Dengr,C.,Ruan,C.,Dai,D.,Guo,D.,etal.Deepseek-v2:
Hendrycks,D.,Burns,C.,Basart,S.,Zou,A.,Mazeika,M., A strong, economical, and efficient mixture-of-experts
Song,D.,andSteinhardt,J. Measuringmassivemultitask language model. arXiv preprint arXiv:2405.04434,
| language | understanding. | In International | Conference | | 2024a. | | | |
| ----------- | ---------------- | ---------------- | ------------ | --- | ------ | --- | --- | --- |
| on Learning | Representations, | 2021. | URL https:// | | | | | |
openreview.net/forum?id=d7KBjmI3GmQ. Liu,H.,Li,C.,Wu,Q.,andLee,Y.J. Visualinstructiontun-
| | | | | | ing. Advancesinneuralinformationprocessingsystems, | | | |
| --------- | ----------------- | -------------- | --------------- | --- | -------------------------------------------------- | --- | --- | --- |
| Jain, N., | Han, K., Gu, | A., Li, W.-D., | Yan, F., Zhang, | T., | 36,2024b. | | | |
| Wang, | S., Solar-Lezama, | A., Sen, | K., and Stoica, | I. | | | | |
Livecodebench: Holistic and contamination free eval- Liu,J.,Liu,H.,Xiao,L.,Wang,Z.,Liu,K.,Gao,S.,Zhang,
uationoflargelanguagemodelsforcode. arXivpreprint W., Zhang, S., and Chen, K. Are your llms capable
| | | | | | ofstablereasoning? | arXivpreprintarXiv:2412.13147, | | |
| --- | --- | --- | --- | --- | ------------------ | ------------------------------ | --- | --- |
arXiv:2403.07974,2024.
2024c.
| Jiang, A. | Q., Sablayrolles, | A., Mensch, | A., Bamford, | C., | | | | |
| --------- | ----------------- | ----------- | ------------ | --- | --- | --- | --- | --- |
Chaplot,D.S.,Casas,D.d.l.,Bressand,F.,Lengyel,G., Lu, P., Mishra, S., Xia, T., Qiu, L., Chang, K.-W., Zhu,
| | | | | | S.-C., Tafjord, | O., Clark, | P., and Kalyan, | A. Learn to |
| --------------------------- | --- | ---------- | ------------- | --- | --------------- | ---------- | --------------- | ----------- |
| Lample,G.,Saulnier,L.,etal. | | Mistral7b. | arXivpreprint | | | | | |
arXiv:2310.06825,2023. explain: Multimodal reasoning via thought chains for
| | | | | | sciencequestionanswering. | | AdvancesinNeuralInfor- | |
| --- | --- | --- | --- | --- | ------------------------- | --- | ---------------------- | --- |
Jimenez,C.E.,Yang,J.,Wettig,A.,Yao,S.,Pei,K.,Press,
mationProcessingSystems,35:2507–2521,2022.
| O.,andNarasimhan,K. | | Swe-bench: | Canlanguagemod- | | | | | |
| ------------------- | --- | ---------- | --------------- | --- | --- | --- | --- | --- |
els resolve real-world github issues? arXiv preprint Lu, P., Bansal, H., Xia, T., Liu, J., Li, C., Hajishirzi,
arXiv:2310.06770,2023. H., Cheng, H., Chang, K.-W., Galley, M., and Gao,
| | | | | | J. Mathvista: | Evaluating | mathematical | reasoning of |
| --- | --- | --- | --- | --- | ------------- | ---------- | ------------ | ------------ |
Kahneman,D. Thinking,fastandslow. Farrar,Strausand foundation models in visual contexts. arXiv preprint
| Giroux,2011. | | | | | arXiv:2310.02255,2023. | | | |
| ------------ | --------------- | ---------- | ---------- | ------- | ---------------------- | --- | ----------- | -------- |
| Kwon, W., | Li, Z., Zhuang, | S., Sheng, | Y., Zheng, | L., Yu, | | | | |
| | | | | | OpenAI. | | Introducing | chatgpt. |
C.H.,Gonzalez,J.E.,Zhang,H.,andStoica,I. Efficient https://openai.com/index/chatgpt/,2022.
memorymanagementforlargelanguagemodelserving
withpagedattention. InProceedingsoftheACMSIGOPS OpenAI. Gpt-4osystemcard. https://openai.com/index/gpt-
4o-system-card/,2024a.
29thSymposiumonOperatingSystemsPrinciples,2023.
Li, B., Wang, R., Wang, G., Ge, Y., Ge, Y., and Shan, Y. OpenAI. Learning to reason with llms.
Seed-bench: Benchmarkingmultimodalllmswithgener- https://openai.com/index/learning-to-reason-with-
llms/,2024b.
| ativecomprehension. | | arXivpreprintarXiv:2307.16125, | | | | | | |
| ------------------- | --- | ------------------------------ | --- | --- | --- | --- | --- | --- |
2023.
Ouyang,L.,Wu,J.,Jiang,X.,Almeida,D.,Wainwright,C.,
Li, B., Zhang, Y., Guo, D., Zhang, R., Li, F., Zhang, H., Mishkin,P.,Zhang,C.,Agarwal,S.,Slama,K.,Ray,A.,
Zhang, K., Zhang, P., Li, Y., Liu, Z., et al. Llava- et al. Training language models to follow instructions
onevision: Easy visual task transfer. arXiv preprint withhumanfeedback. Advancesinneuralinformation
arXiv:2408.03326,2024. processingsystems,35:27730–27744,2022.
Lightman,H.,Kosaraju,V.,Burda,Y.,Edwards,H.,Baker, Pal,A.,Karkhanis,D.,Dooley,S.,Roberts,M.,Naidu,S.,
B., Lee, T., Leike, J., Schulman, J., Sutskever, I., and and White, C. Smaug: Fixing failure modes of pref-
Cobbe, K. Let’s verify step by step. arXiv preprint erence optimisation with dpo-positive. arXiv preprint
| arXiv:2305.20050,2023. | | | | | arXiv:2402.13228,2024. | | | |
| ---------------------- | --- | --- | --- | --- | ---------------------- | --- | --- | --- |
Lin,B.Y.,Deng,Y.,Chandu,K.,Brahman,F.,Ravichander, Pareja, A., Nayak, N. S., Wang, H., Killamsetty, K., Su-
A.,Pyatkin,V.,Dziri,N.,Bras,R.L.,andChoi,Y. Wild- dalairaj, S., Zhao, W., Han, S., Bhandwaldar, A., Xu,
bench: Benchmarkingllmswithchallengingtasksfrom G., Xu, K., etal. Unveilingthesecretrecipe: Aguide
realusersinthewild. arXivpreprintarXiv:2406.04770, for supervised fine-tuning small llms. arXiv preprint
| 2024. | | | | | arXiv:2412.13337,2024. | | | |
| ----- | --- | --- | --- | --- | ---------------------- | --- | --- | --- |
10
R-Bench
Radford,A.,Narasimhan,K.,Salimans,T.,andSutskever, Attentionisallyouneed. InGuyon,I.,Luxburg,U.V.,
I. Improvinglanguageunderstandingbygenerativepre- Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S.,
training. 2018. andGarnett,R.(eds.),AdvancesinNeuralInformation
ProcessingSystems,volume30.CurranAssociates,Inc.,
Radford,A.,Kim,J.W.,Hallacy,C.,Ramesh,A.,Goh,G., 2017. URL https://proceedings.neurips.
Agarwal,S.,Sastry,G.,Askell,A.,Mishkin,P.,Clark,J.,
cc/paper_files/paper/2017/file/
etal. Learningtransferablevisualmodelsfromnatural
3f5ee243547dee91fbd053c1c4a845aa-Paper.
| language | supervision. | | In | International | conference | on | pdf. | | | | | |
| -------- | ------------ | --- | --- | ------------- | ---------- | --- | ---- | --- | --- | --- | --- | --- |
machinelearning,pp.8748–8763.PMLR,2021.
Wang,K.,Pan,J.,Shi,W.,Lu,Z.,Zhan,M.,andLi,H.Mea-
Rafailov,R.,Sharma,A.,Mitchell,E.,Manning,C.D.,Er-
| | | | | | | | suring multimodal | | mathematical | reasoning | with | math- |
| ------------- | --------- | ----------- | ----------- | ---------- | ------------- | --- | ----------------- | --- | ------------------------------------ | --------- | ---- | ----- |
| mon, | S., and | Finn, | C. Direct | preference | optimization: | | | | | | | |
| | | | | | | | visiondataset. | | arXivpreprintarXiv:2402.14804,2024a. | | | |
| Your language | | model | is secretly | a reward | model. | Ad- | | | | | | |
| vances | in Neural | Information | | Processing | Systems, | 36, | | | | | | |
Wang,Y.,Ma,X.,Zhang,G.,Ni,Y.,Chandra,A.,Guo,S.,
| 2024. | | | | | | | Ren,W.,Arulraj,A.,He,X.,Jiang,Z.,etal. | | | | Mmlu-pro: | |
| ----- | --- | --- | --- | --- | --- | --- | -------------------------------------- | --- | --- | --- | --------- | --- |
Amorerobustandchallengingmulti-tasklanguageunder-
| Raffel, C., | Shazeer, | N., | Roberts, | A., Lee, | K., | Narang, S., | | | | | | |
| ----------------------------------- | -------- | --- | -------- | -------- | --- | ----------- | ------------------ | --- | ------------------------------ | --- | --- | --- |
| | | | | | | | standingbenchmark. | | arXivpreprintarXiv:2406.01574, | | | |
| Matena,M.,Zhou,Y.,Li,W.,andLiu,P.J. | | | | | | Exploring | | | | | | |
2024b.
thelimitsoftransferlearningwithaunifiedtext-to-text
| transformer. | | Journalofmachinelearningresearch, | | | | 21 | | | | | | |
| ------------ | --- | --------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
Wei,J.,Wang,X.,Schuurmans,D.,Bosma,M.,Xia,F.,Chi,
(140):1–67,2020.
E.,Le,Q.V.,Zhou,D.,etal.Chain-of-thoughtprompting
Rein, D., Hou, B. L., Stickland, A. C., Petty, J., Pang, elicitsreasoninginlargelanguagemodels. Advancesin
neuralinformationprocessingsystems,35:24824–24837,
| R.Y.,Dirani,J.,Michael,J.,andBowman,S.R. | | | | | | Gpqa: | | | | | | |
| ---------------------------------------- | --- | ------------ | --- | --- | ---------- | ----- | ----- | --- | --- | --- | --- | --- |
| A graduate-level | | google-proof | | q&a | benchmark. | arXiv | 2022. | | | | | |
preprintarXiv:2311.12022,2023.
Wu,Z.,Chen,X.,Pan,Z.,Liu,X.,Liu,W.,Dai,D.,Gao,H.,
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Ma,Y.,Wu,C.,Wang,B.,etal. Deepseek-vl2: Mixture-
Klimov, O. Proximal policy optimization algorithms. of-experts vision-language models for advanced multi-
arXivpreprintarXiv:1707.06347,2017. modalunderstanding. arXivpreprintarXiv:2412.10302,
2024.
| Sun, Q., | Yu, Q., | Cui, | Y., Zhang, | F., Zhang, | X., | Wang, Y., | | | | | | |
| -------- | ------- | ---- | ---------- | ---------- | --- | --------- | --- | --- | --- | --- | --- | --- |
Gao,H.,Liu,J.,Huang,T.,andWang,X. Emu: Genera- Yang, A., Yang, B., Zhang, B., Hui, B., Zheng, B., Yu,
tivepretraininginmultimodality. InTheTwelfthInterna- B.,Li,C.,Liu,D.,Huang,F.,Wei,H.,etal. Qwen2.5
tionalConferenceonLearningRepresentations,2023.
| | | | | | | | technicalreport. | | arXivpreprintarXiv:2412.15115,2024. | | | |
| ---------- | ---------- | --- | ---------- | ----------- | --- | ----------- | ---------------- | --- | ----------------------------------- | --- | --- | --- |
| Taori, R., | Gulrajani, | | I., Zhang, | T., Dubois, | | Y., Li, X., | | | | | | |
Yu,W.,Yang,Z.,Li,L.,Wang,J.,Lin,K.,Liu,Z.,Wang,
Guestrin,C.,Liang,P.,andHashimoto,T.B. Stanford X., and Wang, L. Mm-vet: Evaluating large multi-
| alpaca: | Aninstruction-followingllamamodel,2023. | | | | | | | | | | | |
| ------- | --------------------------------------- | --- | --- | --- | --- | --- | ------------------------------------- | --- | --- | --- | ------------- | --- |
| | | | | | | | modalmodelsforintegratedcapabilities. | | | | arXivpreprint | |
arXiv:2308.02490,2023.
Team,G.,Anil,R.,Borgeaud,S.,Alayrac,J.-B.,Yu,J.,Sori-
cut,R.,Schalkwyk,J.,Dai,A.M.,Hauth,A.,Millican, Yue,X.,Ni,Y.,Zhang,K.,Zheng,T.,Liu,R.,Zhang,G.,
| K.,etal. | Gemini: | afamilyofhighlycapablemultimodal | | | | | | | | | | |
| -------- | ----------------------------------- | -------------------------------- | --- | --- | --- | --- | --------------------------------------- | --- | --- | --- | --- | ------- |
| | | | | | | | Stevens,S.,Jiang,D.,Ren,W.,Sun,Y.,etal. | | | | | Mmmu: A |
| models. | arXivpreprintarXiv:2312.11805,2023. | | | | | | | | | | | |
massivemulti-disciplinemultimodalunderstandingand
Team, G., Georgiev, P., Lei, V. I., Burnell, R., Bai, L., reasoningbenchmarkforexpertagi.InProceedingsofthe
IEEE/CVFConferenceonComputerVisionandPattern
| Gulati, | A., Tanzer, | | G., Vincent, | D., | Pan, Z., | Wang, S., | | | | | | |
| ------- | ----------- | --- | ------------ | --- | -------- | --------- | --- | --- | --- | --- | --- | --- |
Recognition,pp.9556–9567,2024a.
| et al. | Gemini | 1.5: | Unlocking | multimodal | | understand- | | | | | | |
| ------ | ------ | ---- | --------- | ---------- | --- | ----------- | --- | --- | --- | --- | --- | --- |
arXivpreprint
ingacrossmillionsoftokensofcontext.
Yue,X.,Zheng,T.,Ni,Y.,Wang,Y.,Zhang,K.,Tong,S.,
arXiv:2403.05530,2024.
| | | | | | | | Sun,Y.,Yu,B.,Zhang,G.,Sun,H.,etal. | | | | Mmmu-pro: | A |
| --- | --- | --- | --- | --- | --- | --- | ---------------------------------- | --- | --- | --- | --------- | --- |
Touvron,H.,Lavril,T.,Izacard,G.,Martinet,X.,Lachaux, morerobustmulti-disciplinemultimodalunderstanding
M.-A.,Lacroix,T.,Rozie`re,B.,Goyal,N.,Hambro,E., benchmark. arXivpreprintarXiv:2409.02813,2024b.
| Azhar,F.,etal. | | Llama:Openandefficientfoundationlan- | | | | | | | | | | |
| -------------- | --- | ------------------------------------ | --- | --- | --- | --- | ----------------------------------- | --- | ------------- | --- | --------- | --------- |
| | | | | | | | Zeng, A., Liu, | X., | Du, Z., Wang, | Z., | Lai, H., | Ding, M., |
| guagemodels. | | arXivpreprintarXiv:2302.13971,2023. | | | | | | | | | | |
| | | | | | | | Yang,Z.,Xu,Y.,Zheng,W.,Xia,X.,etal. | | | | Glm-130b: | |
Vaswani,A.,Shazeer,N.,Parmar,N.,Uszkoreit,J.,Jones, An open bilingual pre-trained model. arXiv preprint
L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I. arXiv:2210.02414,2022.
11
R-Bench
| Zheng,L.,Chiang,W.-L.,Sheng,Y.,Zhuang,S.,Wu,Z., | | | | A.Appendix. | | | | | | |
| ----------------------------------------------- | --- | --- | ------- | ----------- | --- | --- | --- | --- | --- | --- |
| Zhuang,Y.,Lin,Z.,Li,Z.,Li,D.,Xing,E.,etal. | | | Judging | | | | | | | |
A.1.ResponseExample
| llm-as-a-judge | with mt-bench | and chatbot | arena. Ad- | | | | | | | |
| -------------- | ------------- | ----------- | ---------- | --- | --- | --- | --- | --- | --- | --- |
vances in Neural Information Processing Systems, 36: Here, wepresentourquestionandtheoutputoftheOpe-
46595–46623,2023.
| | | | | nAI’so1modelintheboxbelow. | | | | Themodelcarriedouta | | |
| --- | --- | --- | --- | -------------------------- | --- | --- | --- | ------------------- | --- | --- |
step-by-stepanddetailedreasoningbasedonthequestion
| Zhu, D., | Chen, J., Shen, | X., Li, X., | and Elhoseiny, M. | | | | | | | |
| -------- | --------------- | ----------- | ----------------- | --- | --- | --- | --- | --- | --- | --- |
andfinallyarrivedatthecorrectanswer.
| Minigpt-4: | Enhancing | vision-language | understanding | | | | | | | |
| ------------- | -------------- | --------------- | -------------- | --- | --- | --- | --- | --- | --- | --- |
| with advanced | large language | models. | arXiv preprint | | | | | | | |
Questionandtheresponsebyo1
arXiv:2304.10592,2023.
| | | | | Answer | the | following | single choice | question. | | The |
| --- | --- | --- | --- | ------ | --- | --------- | ------------- | --------- | --- | --- |
lastlineofyourresponseshouldbeofthefollowing
| | | | | format: | ’ANSWER: | | $LETTER’ | (without | quotes) | |
| --- | --- | --- | --- | ----------------------------------------- | ----------- | ---------- | --------------- | -------- | ---------- | ------ |
| | | | | where | LETTER | is one | of Options(e.g. | | | one of |
| | | | | ABCDEF). | | Think step | by step | before | answering. | |
| | | | | Question: | | A sudden | environmental | change | | causes |
| | | | | arecessivealleletobecomecompletelylethal. | | | | | | How |
| | | | | many | generations | will | it take | for its | frequency | to |
| | | | | decrease | from | 10−2 to | 10−3? | | | |
A. 950
B. 850
| | | | | C. | All other | answers are | incorrect | | | |
| --- | --- | --- | --- | --- | --------- | ----------- | --------- | --- | --- | --- |
D. 900
E. 800
F. 920
Answer:
| | | | | ### | Response | by o1 | | | | |
| --- | --- | --- | --- | ---------------------------------- | ----------- | ------- | --------- | -------------- | --------- | --- |
| | | | | To | solve this, | use the | fact that | for a fully | recessive | |
| | | | | lethalallele(withinitialfrequencyq | | | | ),itsfrequency | | |
0
| | | | | in the | next | generation | is given | by: | | |
| --- | --- | --- | --- | ------ | ---- | ---------- | -------- | --- | --- | --- |
q
| | | | | | | q | = n | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
n+1
1+q n
1
| | | | | Define | R n | = . Then | from the | recurrence | | above: |
| --- | --- | --- | --- | ------ | --- | -------- | -------- | ---------- | --- | ------ |
qn
1
| | | | | | | R | = | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | | | | | n+1 | q | | | |
n+1
1+q n
=
q
n
1
| | | | | | | | = +1 | | | |
| --- | --- | --- | --- | --- | --- | --- | ---- | --- | --- | --- |
q n
| | | | | | | | =R +1 | | | |
| --- | --- | --- | --- | --- | --- | --- | ----- | --- | --- | --- |
n
| | | | | Thus,R | =R | +n. SinceR | | = 1 andwewantq | | |
| --- | --- | --- | --- | ----------- | ------- | ----------- | --------- | -------------- | --- | --- |
| | | | | | n | 0 | 0 | q0 | | n |
| | | | | to decrease | | from 0.01 | to 0.001: | | | |
| | | | | • | R = | 1 =100. | | | | |
| | | | | | 0 | 0.01 | | | | |
| | | | | • | We need | 1 =1000, | so R | =1000. | | |
| | | | | | | qn | | n | | |
| | | | | • | Hence | 1000=100+n, | giving | n=900. | | |
| | | | | ANSWER: | | D | | | | |
A.2.CoTPrompt
Here,wepresenttheformatofourCoTpromptinfollowing
box. Aswecansee,ourCoTpromptmainlyuses“Think
stepbystep”,whichshowsthatevenasimplepromptstill
12
R-Bench
hasapositiveeffectonmostchatmodels.
SystemPromptforR-Bench
Answer the following single-choice question. The
lastlineofyourresponseshouldbeofthefollowing
format: ’ANSWER: $LETTER’ (without quotes)
where LETTER is one of the Options (e.g. one of
ABCDEF). Think step by step before answering.
Question: {Question Input}
A. {OptionA}
B. {OptionB}
C. {OptionC}
D. {OptionD}
E. {OptionE}
F. {OptionF}
Answer:
### Example:
Answer the following single choice question. The
lastlineofyourresponseshouldbeofthefollowing
format: ‘ANSWER: $LETTER’ (without quotes)
where LETTER is one of Options(e.g. one of
ABCDEF). Think step by step before answering.
Question: ConsideraCMOSinverterdrivingawire
of length L. In the initial design, the on-resistance
oftheinverterisequaltothetotalresistanceofthe
wire, the source-drain capacitance of the inverter
is equal to the total capacitance of the wire, and
the total delay of the inverter and wire is tp. Now,
the devices are scaled down using Constant Field
Scaling, while the wire is ideally scaled down.
Assumingthewirecanbemodeledusingalumped
parameter model, answer the following questions
in a first-order approximation:
(1) Assuming the wire is a local wire, and the
scaling factors for both process and supply voltage
are 2, express the total delay after scaling in terms
of tp.
(2) Now assume the wire is global, and the length
of the wire increases inversely with the process
scaling, with scaling factors for both process and
supply voltage being 2, express the total delay
after scaling in terms of tp.
A. (1) 1/3tp (2) 35/6tp
B. (1) 1/2tp (2) 38/6tp
C. All other answers are incorrect
D. (1) 3/4tp (2) 36/6tp
E. (1) 5/6tp (2) 39/6tp
F. (1) 2/3tp (2) 37/6tp
Answer:
A.3.Specificsubjectdistribution
WepresentthespecificsubjectdistributionsofR-Bench-T
andR-Bench-MinTable8andTable8,respectively. Itcan
beobservedthatR-Benchhasabroadcoverage,makingit
difficulttoimproveperformanceonR-Benchbyoverfitting
tospecificsubjects.
13
R-Bench
DistributionofCoursesbyDisciplineinR-Bench-T
Table8:
| Discipline | SpecificSubject | Count |
| ------------------- | ------------------------------- | ----- |
| | FluidMechanics | 33 |
| CivilEngineering | StructuralMechanics | 2 |
| | Surveying | 1 |
| | PrinciplesofProcessTransport | 3 |
| ChemicalEngineering | PrinciplesofChemicalEngineering | 15 |
| | AnalyticalChemistry | 7 |
| | AdvancedMathematicalEconomics | 1 |
| | Econometrics | 3 |
| | IntermediateFinancialTheory | 1 |
| Economics | FinancialEngineering | 1 |
| | GameTheoryandMechanismDesign | 5 |
| | IntermediateMicroeconomics | 2 |
| | PrinciplesofAccounting | 2 |
| | TimeSeriesAnalysis | 8 |
| | SoilScience | 1 |
| | Genetics | 16 |
Biology
| | Physiology | 1 |
| ------- | ----------------------------------- | --- |
| | Biochemistry | 15 |
| | Heredity | 8 |
| | MathematicalMethodsinPhysics | 10 |
| | Electromagnetics | 11 |
| | Optics | 23 |
| | QuantumMechanics | 8 |
| Physics | AnalyticalMechanics | 6 |
| | Electrodynamics | 5 |
| | ThermodynamicsandStatisticalPhysics | 6 |
| | GeneralRelativity | 2 |
| | BasicPhysics | 29 |
| | GroupTheory | 3 |
| | MechanicsofMaterials | 1 |
| | StructuralMechanicsofAircraft | 3 |
| | TheoreticalMechanics | 2 |
Aerospace
| | FluidMechanicsandAerodynamics | 15 |
| --- | ----------------------------------------- | --- |
| | OptimalControl | 29 |
| | PropulsionPrinciplesandThermalFluidBasics | 9 |
| | DigitalLarge-ScaleIntegratedCircuits | 20 |
Microelectronics
| | AnalogCircuits | 2 |
| ---------- | ------------------------------- | --- |
| | SignalsandSystems | 20 |
| Automation | OperationsResearch | 15 |
| | AutomaticControlTheory | 17 |
| | CommunicationandNetwork | 15 |
| | PrinciplesofAnalogCircuits | 4 |
| | ElectromagneticFieldsandWaves | 8 |
| | FundamentalsofSolidStatePhysics | 12 |
ElectronicEngineering
| | DigitalSignalProcessing | 8 |
| --- | ----------------------- | --- |
| | StochasticProcesses | 3 |
Continuedonnextpage
14
R-Bench
Table8–Continuedfrompreviouspage
| Discipline | SpecificSubject | Count |
| ---------- | ---------------------------------------------------------- | ----- |
| | SolidStatePhysics | 4 |
| | AppliedStochasticProcesses | 26 |
| | FluidMechanics | 11 |
| | PrinciplesandInterfaceTechnologyofSingle-ChipMicrocomputer | 4 |
| | MechanicalDesign | 1 |
| | TheoryofMachines | 4 |
| | ElectromechanicalTransmissionandControl | 4 |
MechanicalEngineering
| | ElectricalandelectronicTechnology | 2 |
| --------------- | ------------------------------------ | --- |
| | MechanicalVibration | 3 |
| | HydraulicandPneumaticTransmission | 1 |
| | EngineeringThermodynamics | 8 |
| | MechanicsofMaterials | 9 |
| | TheoreticalMechanics | 1 |
| | DataStructure | 24 |
| | CombinatorialMathematics | 3 |
| | NumericalAnalysis | 5 |
| | Cryptography | 17 |
| ComputerScience | Automata | 8 |
| | PrinciplesofComputerOrganization | 2 |
| | CompilationPrinciples | 5 |
| | ComputerNetwork | 11 |
| | OperatingSystem | 11 |
| | ComputerArchitecture | 3 |
| | InorganicChemistry | 50 |
| | ChemicalThermodynamics | 48 |
| | ChemicalReactionKinetics | 20 |
| | IntroductiontoComputationalChemistry | 11 |
Chemistry
| | QuantumChemistry | 5 |
| --- | ---------------------------- | --- |
| | PhysicalChemistry | 22 |
| | OrganicChemistry | 5 |
| | ComplexAnalysis | 26 |
| | AnalyticGeometry | 26 |
| | AdvancedCalculus | 9 |
| | NumberTheory | 22 |
| | MatrixAnalysis | 36 |
| | PartialDifferentialEquations | 29 |
Mathematics
| | MathematicalAnalysis | 9 |
| --- | ----------------------------------- | --- |
| | StochasticDifferentialEquations | 8 |
| | FunctionalAnalysis | 14 |
| | OrdinaryDifferentialEquations | 3 |
| | DifferentialGeometry | 3 |
| | Topology | 3 |
| | ThermodynamicsandStatisticalPhysics | 5 |
PhysicsEngineering
| | NuclearRadiationPhysicsandDetection | 20 |
| --- | ----------------------------------- | --- |
| | QuantumandStatistics | 14 |
| | PhysicalPropertiesofMaterials | 1 |
Materials
| | FundamentalsofMaterialsScience | 23 |
| --- | ------------------------------ | --- |
Continuedonnextpage
15
R-Bench
Table8–Continuedfrompreviouspage
| Discipline | SpecificSubject | Count |
| ------------------ | ------------------------------------ | ----- |
| | MaterialsAnalysisandCharacterization | 3 |
| | FiniteElementAnalysisBasics | 1 |
| | PrinciplesofAutomotivePowerSystem | 11 |
| | AutomotiveElectronicsandControl | 1 |
| VehicleEngineering | FundamentalsofControlEngineering | 3 |
| | TheoryofAutomobile | 3 |
| | AutomobileConstruction | 1 |
| | DiscreteMathematics | 12 |
| | ProbabilityTheory | 42 |
| | IntroductiontoBayesianStatistics | 6 |
Statistics
| | ReliabilityDataandSurvivalAnalysis | 1 |
| ----------- | --------------------------------------------------------- | --- |
| | StatisticalInference | 2 |
| | PrinciplesofEnvironmentalEngineeringScienceandEngineering | 5 |
| Environment | WaterTreatmentEngineering | 12 |
| | EnvironmentalChemistry | 1 |
16
R-Bench
DistributionofCoursesbyDisciplineinR-Bench-M
Table9:
| Discipline | SpecificSubject | Count |
| ---------- | ----------------------------- | ----- |
| | MaterialsMechanics | 26 |
| | FluidMechanicsandAerodynamics | 2 |
| | TheoreticalMechanics | 16 |
Aerospace
| | AircraftStructuralMechanics | 2 |
| --- | ------------------------------------ | --- |
| | PropulsionPrinciplesandThermalFluids | 2 |
| | OptimalControl | 2 |
| | DigitalVLSI | 11 |
| | AnalogCircuits | 19 |
IntegratedCircuits
| | DigitalElectronicsFundamentals | 8 |
| ------------------- | ------------------------------ | --- |
| | AnalogElectronicsFundamentals | 6 |
| | TransportProcessPrinciples | 2 |
| ChemicalEngineering | ChemicalPrinciples | 11 |
| | PhysicalChemistry | 12 |
| | ChemicalThermodynamics | 1 |
| | ChemicalReactionKinetics | 10 |
| Chemistry | InorganicChemistry | 1 |
| | OrganicChemistry | 17 |
| | PhysicalChemistry | 2 |
| | DataStructures | 4 |
| | Combinatorics | 1 |
| | DiscreteMathematics | 12 |
| | TheoryofAutomata | 7 |
| ComputerScience | OperatingSystems | 6 |
| | Compilers | 4 |
| | ComputerArchitecture | 3 |
| | Cryptography | 1 |
| | ComputerNetworks | 1 |
| | AnalyticalMechanics | 25 |
| | Optics | 6 |
| Physics | Electrodynamics | 9 |
| | Electromagnetism | 10 |
| | BasicPhysics | 10 |
| | AnalogCircuitPrinciples | 17 |
| | SignalsandSystems | 3 |
| | DigitalSignalProcessing | 1 |
ElectricalEngineering
| | CommunicationandNetworks | 4 |
| ----------- | ----------------------------- | --- |
| | ElectromagneticFieldsandWaves | 1 |
| | SolidStatePhysics | 1 |
| | ComplexAnalysis | 8 |
| | AnalyticGeometry | 2 |
| | ProbabilityTheory | 5 |
| | StochasticProcesses | 3 |
| Mathematics | Analysis | 1 |
| | ProbabilityandStatistics | 1 |
| | MathematicalAnalysis | 5 |
| | Statistics | 6 |
Continuedonnextpage
17
R-Bench
Table9–Continuedfrompreviouspage
| Discipline | SpecificSubject | Count |
| ---------- | -------------------------------------------- | ----- |
| | Topology | 1 |
| | WaterTreatmentEngineering | 4 |
| | EnvironmentalScienceandEngineeringPrinciples | 9 |
| | EnvironmentalMonitoring | 6 |
EnvironmentalEngineering
| | WaterPollutionControlProject | 4 |
| --- | ------------------------------ | --- |
| | EnvironmentalChemistry | 1 |
| | SolidWasteTreatmentandDisposal | 2 |
| | Genetics | 13 |
Biology
| | Biochemistry | 4 |
| --- | ------------------------------ | --- |
| | MaterialsMechanics | 37 |
| | QuantumandStatisticalMechanics | 5 |
MaterialScience
| | MaterialAnalysisandCharacterization | 1 |
| --------- | --------------------------------------- | --- |
| | BasicMaterialScience | 18 |
| | OperationsResearch | 2 |
| | PrinciplesofAccounting | 3 |
| Economics | FinancialEngineering | 2 |
| | IntermediateFinancialTheories | 3 |
| | GameTheoryandMechanismDesign | 8 |
| | ElectricalandElectronicsTechnology | 5 |
| | MechanicalDesign | 24 |
| | ElectromechanicalTransmissionandControl | 1 |
| | HydraulicandPneumaticTransmission | 9 |
MechanicalEngineering
| | MechanicalVibrations | 7 |
| ------------------ | ------------------------------- | --- |
| | FluidMechanics | 7 |
| | PrinciplesofMechanics | 1 |
| | TheoreticalMechanics | 11 |
| CivilEngineering | StructuralMechanics | 33 |
| EngineeringPhysics | EngineeringMechanics | 23 |
| Automation | AutomaticControlTheory | 25 |
| | FluidMechanics | 34 |
| | EngineeringControlBasics | 8 |
| | FiniteElementAnalysisBasics | 5 |
| VehicleEngineering | AutomotiveElectronicsandControl | 5 |
| | AutomobileConstruction | 5 |
| | AdvancedHeatTransfer | 9 |
| | AutomotivePowerSystemPrinciples | 2 |
| Architecture | StructuralEngineering | 21 |
18 |