GPT-4 · ARC AI2 · Challenge score · % ↗
OpenAI / MODEL EVIDENCE
GPT-4
44 benchmarks · 129 observations · 39 sources
Scores at a glance
44 benchmarks · large dot: GPT-4; small dots: closest models where reported
GPT-4 · GSM8K · EM · % ↗
GPT-4 · MMLU · EM · % ↗
GPT-4 · PutnamBench · Proof success · Lean ↗
2 more reported results
0.1562% · GPT-4 · PutnamBench · Proof success · Isabelle · PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition ↗
0.2427% · GPT-4 · PutnamBench · Proof success · Coq · PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition ↗
GPT-4 · GSM-Plus / GSM-Symbolic · GSM-Plus accuracy · Original robustness evaluation ↗
GPT-4 · CollegeMath · CollegeMath accuracy · MathScale paper · MwpBench evaluation ↗
GPT-4 · MathOdyssey · Overall accuracy · original model comparison ↗
GPT-4 · OlympiadBench (Math) · Mathematics · Text-only problems ↗
GPT-4 · OlympiadBench · physics · Text-only problems ↗
GPT-4 · FActScore · estimator A · Unlabeled entity-biography evaluation ↗
GPT-4 · C-Eval · Accuracy · Zero-shot ↗
1 more reported result
68.7% · GPT-4 · C-Eval · Accuracy · Five-shot · C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models ↗
GPT4 · LawBench · Task average · Zero-shot ↗
1 more reported result
53.85% · GPT4 · LawBench · Task average · One-shot · LawBench: Benchmarking Legal Knowledge of Large Language Models ↗
GPT-4 · Do-Not-Answer & BeaverTails · human-rated safety · Original safeguard evaluation ↗
GPT-4 · PlanBench · Blocksworld ↗
1 more reported result
0% · GPT-4 · PlanBench · Mystery Blocksworld · PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change ↗
GPT-4 · MuSR (Multistep Soft Reasoning) · Accuracy · Murder mysteries · CoT+ ↗
2 more reported results
60.9% · GPT-4 · MuSR (Multistep Soft Reasoning) · Accuracy · Object placement · CoT+ · MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning ↗
68.4% · GPT-4 · MuSR (Multistep Soft Reasoning) · Accuracy · Team allocation · CoT+ · MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning ↗
GPT4 · InfiniteBench · InfiniteBench aggregate · HELMET paper · original-author harness ↗
GPT-4 · CriticBench · Critiquing · zero-shot answer-only ↗
2 more reported results
78.75% · GPT-4 · CriticBench · Critiquing · zero-shot CoT · CriticBench: Benchmarking LLMs for Critique-Correct Reasoning ↗
56.65% · GPT-4 · CriticBench · Correction · zero-shot CoT · CriticBench: Benchmarking LLMs for Critique-Correct Reasoning ↗
GPT-4 · Collie &Conifer · Zero-shot ↗
1 more reported result
39.4% · GPT-4 · Collie &Conifer · One-shot · COLLIE: Systematic Construction of Constrained Text Generation Tasks ↗
GPT-4 · JailbreakBench (JBB-Behaviors) · JBB original-source attack success · source-subset comparison ↗
3 more reported results
2% · GPT-4 · JailbreakBench (JBB-Behaviors) · JBB original-source attack success · source-subset comparison · JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models ↗
0% · GPT-4 · JailbreakBench (JBB-Behaviors) · JBB original-source attack success · source-subset comparison · JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models ↗
73% · GPT-4 · JailbreakBench (JBB-Behaviors) · JBB original-source attack success · source-subset comparison · JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models ↗
GPT-4 · BIPIA & InjecAgent (Indirect Prompt Injection) · BIPIA overall attack success · black-box defense comparison ↗
2 more reported results
24.08% · GPT-4 · BIPIA & InjecAgent (Indirect Prompt Injection) · BIPIA overall attack success · black-box defense comparison · Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models ↗
20.56% · GPT-4 · BIPIA & InjecAgent (Indirect Prompt Injection) · BIPIA overall attack success · black-box defense comparison · Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models ↗
GPT-4 · HumanEval+ · Pass@1 · Llama 3 report · post-trained models ↗
GPT-4 · HumanEval · Pass@1 · Llama 3 report · post-trained models ↗
GPT-4 · WinoGrande · WinoGrande accuracy ↗
GPT-4 · ARC-Challenge (AI2 Reasoning Challenge) · ARC-Challenge accuracy · Llama 3 report · pretrained models ↗
GPT-4 · DROP (Discrete Reasoning Over Paragraphs) · F1 · Llama 3 report · pretrained models ↗
GPT-4 · VisualWebArena · Overall task success · original agents ↗
1 more reported result
12.75% · GPT-4 · VisualWebArena · Overall task success · original agents · VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks ↗
GPT4 · GAIA · Level 1 · accuracy · original baselines ↗
2 more reported results
2.6% · GPT4 · GAIA · Level 2 · accuracy · original baselines · GAIA: a benchmark for General AI Assistants ↗
0% · GPT4 · GAIA · Level 3 · accuracy · original baselines · GAIA: a benchmark for General AI Assistants ↗
GPT4 · CMMLU · Five-shot subject-macro accuracy · original five-shot comparison ↗
GPT-4 · Putnam-AXIOM · Original · problem accuracy · original questions ↗
GPT-4 · ChemBench · Overall accuracy · published model comparison ↗
1 more reported result
64% · GPT-4 · ChemBench · Human-evaluation subset · accuracy · published model comparison · Are large language models superhuman chemists? ↗
GPT 4* / - · BizBench · Published task average · zero-shot and few-shot evaluation ↗
GPT-4 · BhasaBench / IndicGenBench · CrossSum-In · one-shot evaluation ↗
7 more reported results
17.6 points · GPT-4 · BhasaBench / IndicGenBench · CrossSum-In · one-shot evaluation · IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages ↗
32.1 points · GPT-4 · BhasaBench / IndicGenBench · Flores-In · one-shot evaluation · IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages ↗
64.8 points · GPT-4 · BhasaBench / IndicGenBench · XQuAD-In · one-shot evaluation · IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages ↗
55.7 points · GPT-4 · BhasaBench / IndicGenBench · XQuAD-In · one-shot evaluation · IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages ↗
23.4 points · GPT-4 · BhasaBench / IndicGenBench · XorQA-In-Xx · one-shot evaluation · IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages ↗
37.9 points · GPT-4 · BhasaBench / IndicGenBench · XorQA-In-En · one-shot evaluation · IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages ↗
46 points · GPT-4 · BhasaBench / IndicGenBench · XorQA-In-En · one-shot evaluation · IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages ↗
GPT-4 · WildGuardTest · Prompt harmfulness · total F1 · original moderation comparison ↗
2 more reported results
77.3 F1 points · GPT-4 · WildGuardTest · Response harmfulness · total F1 · original moderation comparison · WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs ↗
92.4 F1 points · GPT-4 · WildGuardTest · Refusal detection · total F1 · original moderation comparison · WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs ↗
GPT-4 · WebArena · End-to-end task success · original prompting configurations ↗
1 more reported result
14.41% · GPT-4 · WebArena · End-to-end task success · original prompting configurations · WebArena: A Realistic Web Environment for Building Autonomous Agents ↗
GPT-4 · XSTest (Exaggerated Safety Test) · Safe prompts · fully refusing responses · human annotations ↗
1 more reported result
97.5% · GPT-4 · XSTest (Exaggerated Safety Test) · Unsafe prompts · fully refusing responses · human annotations · XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models ↗
GPT-4 · SciBench · Textbook overall accuracy · prompting and tool conditions ↗
3 more reported results
28.52% · GPT-4 · SciBench · Textbook overall accuracy · prompting and tool conditions · SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models ↗
30.36% · GPT-4 · SciBench · Textbook overall accuracy · prompting and tool conditions · SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models ↗
43.22% · GPT-4 · SciBench · Textbook overall accuracy · prompting and tool conditions · SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models ↗
GPT-4 · MT-Bench & MT-Bench-101 · GPT-4 judge score · original model variants ↗
GPT-4 · FreshQA · Relaxed with search · overall accuracy · source April 2023 snapshot ↗
GPT-4 · CoCoNot (Contextual Non-Compliance) · No system prompt · Incomplete · compliance rate · reported model comparison ↗
11 more reported results
11.5% · GPT-4 · CoCoNot (Contextual Non-Compliance) · No system prompt · Unsupported · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
14.1% · GPT-4 · CoCoNot (Contextual Non-Compliance) · No system prompt · Indeterminate · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
11.4% · GPT-4 · CoCoNot (Contextual Non-Compliance) · No system prompt · Safety · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
6.1% · GPT-4 · CoCoNot (Contextual Non-Compliance) · No system prompt · Humanizing · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
97.4% · GPT-4 · CoCoNot (Contextual Non-Compliance) · No system prompt · Contrast set · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
19.6% · GPT-4 · CoCoNot (Contextual Non-Compliance) · With system prompt · Incomplete · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
3.2% · GPT-4 · CoCoNot (Contextual Non-Compliance) · With system prompt · Unsupported · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
0% · GPT-4 · CoCoNot (Contextual Non-Compliance) · With system prompt · Indeterminate · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
0.3% · GPT-4 · CoCoNot (Contextual Non-Compliance) · With system prompt · Safety · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
2.4% · GPT-4 · CoCoNot (Contextual Non-Compliance) · With system prompt · Humanizing · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
94.7% · GPT-4 · CoCoNot (Contextual Non-Compliance) · With system prompt · Contrast set · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
gpt4 · AlpacaEval · AlpacaEval 1 · GPT-4 judged win rate · original leaderboard example ↗
GPT-4 · DarkBench · DarkScore · six-category mean · publisher website comparison ↗
6 more reported results
13% · GPT-4 · DarkBench · Anthropomorphization · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗
64% · GPT-4 · DarkBench · Brand bias · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗
71% · GPT-4 · DarkBench · Harmful generation · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗
72% · GPT-4 · DarkBench · Sneaking · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗
9% · GPT-4 · DarkBench · Sycophancy · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗
65% · GPT-4 · DarkBench · User retention · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗
GPT-4 · ClinicBench · MedQA · Accuracy · zero-shot main comparison ↗
16 more reported results
78.2% · GPT-4 · ClinicBench · MedMCQA · Accuracy · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
92.3% · GPT-4 · ClinicBench · MMLU · Accuracy · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
80% · GPT-4 · ClinicBench · PubMedQA · Accuracy · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
83.2% · GPT-4 · ClinicBench · Referral QA · Accuracy · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
18.6 F1 points · GPT-4 · ClinicBench · Treat Recom. · F1 · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
20.7 ROUGE-L points · GPT-4 · ClinicBench · MIMIC · ROUGE-L · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
18.6 ROUGE-L points · GPT-4 · ClinicBench · IU-Xray · ROUGE-L · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
14.2 ROUGE-L points · GPT-4 · ClinicBench · Hospitaliz. Summari. · ROUGE-L · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
12.7 ROUGE-L points · GPT-4 · ClinicBench · Patient Education · ROUGE-L · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
71.3 F1 entity-level points · GPT-4 · ClinicBench · BC5 · F1 entity-level · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
58.4 F1 entity-level points · GPT-4 · ClinicBench · NCBI · F1 entity-level · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
64.6 Micro F1 points · GPT-4 · ClinicBench · DDI · Micro F1 · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
68.2 Micro F1 points · GPT-4 · ClinicBench · GAD · Micro F1 · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
83.6 Micro F1 points · GPT-4 · ClinicBench · HoC · Micro F1 · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
63.8% · GPT-4 · ClinicBench · Pharma. QA · Accuracy · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
56.5% · GPT-4 · ClinicBench · Drug Inter. · Accuracy · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
GPT-4 · WikiContradict · All instances · Fully correct · Llama-3-70B judge · template 5 ↗
17 more reported results
73.5% · GPT-4 · WikiContradict · All instances · Partially correct · Llama-3-70B judge · template 5 · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
11.5% · GPT-4 · WikiContradict · All instances · Incorrect · Llama-3-70B judge · template 5 · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
19.3% · GPT-4 · WikiContradict · Explicit contradictions · Fully correct · Llama-3-70B judge · template 5 · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
70.8% · GPT-4 · WikiContradict · Explicit contradictions · Partially correct · Llama-3-70B judge · template 5 · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
9.9% · GPT-4 · WikiContradict · Explicit contradictions · Incorrect · Llama-3-70B judge · template 5 · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
7.6% · GPT-4 · WikiContradict · Implicit contradictions · Fully correct · Llama-3-70B judge · template 5 · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
78.3% · GPT-4 · WikiContradict · Implicit contradictions · Partially correct · Llama-3-70B judge · template 5 · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
14.1% · GPT-4 · WikiContradict · Implicit contradictions · Incorrect · Llama-3-70B judge · template 5 · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
2.1% · GPT-4 · WikiContradict · WikiContradict HumanEval · Template 1 · All · fully correct · human annotation · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
0% · GPT-4 · WikiContradict · WikiContradict HumanEval · Template 1 · Explicit · fully correct · human annotation · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
5.9% · GPT-4 · WikiContradict · WikiContradict HumanEval · Template 1 · Implicit · fully correct · human annotation · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
6.3% · GPT-4 · WikiContradict · WikiContradict HumanEval · Template 4 · All · fully correct · human annotation · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
3.3% · GPT-4 · WikiContradict · WikiContradict HumanEval · Template 4 · Explicit · fully correct · human annotation · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
11.8% · GPT-4 · WikiContradict · WikiContradict HumanEval · Template 4 · Implicit · fully correct · human annotation · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
10.4% · GPT-4 · WikiContradict · WikiContradict HumanEval · Template 5 · All · fully correct · human annotation · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
10% · GPT-4 · WikiContradict · WikiContradict HumanEval · Template 5 · Explicit · fully correct · human annotation · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
11.8% · GPT-4 · WikiContradict · WikiContradict HumanEval · Template 5 · Implicit · fully correct · human annotation · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
GPT-4 · SycophancyEval · Answer changed after challenge · Sycophancy evaluations · Are you sure? ↗
1 more reported result
42% · GPT-4 · SycophancyEval · Mistake admitted after challenge · Sycophancy evaluations · Are you sure? · Towards Understanding Sycophancy in Language Models ↗
Closest models by score
Similar positions on shared benchmark leaderboards. Smaller gaps are closer.
There are not enough shared scored settings to identify close comparisons reliably.
Sources
Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.
- GPT-4 Technical Report ↗
All 281 authors
OpenAI, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, Red Avila, Igor Babuschkin, Suchir Balaji, Valerie Balcom, Paul Baltescu, Haiming Bao, Mohammad Bavarian, Jeff Belgum, Irwan Bello, Jake Berdine, Gabriel Bernadett-Shapiro, Christopher Berner, Lenny Bogdonoff, Oleg Boiko, Madelaine Boyd, Anna-Luisa Brakman, Greg Brockman, Tim Brooks, Miles Brundage, Kevin Button, Trevor Cai, Rosie Campbell, Andrew Cann, Brittany Carey, Chelsea Carlson, Rory Carmichael, Brooke Chan, Che Chang, Fotis Chantzis, Derek Chen, Sully Chen, Ruby Chen, Jason Chen, Mark Chen, Ben Chess, Chester Cho, Casey Chu, Hyung Won Chung, Dave Cummings, Jeremiah Currier, Yunxing Dai, Cory Decareaux, Thomas Degry, Noah Deutsch, Damien Deville, Arka Dhar, David Dohan, Steve Dowling, Sheila Dunning, Adrien Ecoffet, Atty Eleti, Tyna Eloundou, David Farhi, Liam Fedus, Niko Felix, Simón Posada Fishman, Juston Forte, Isabella Fulford, Leo Gao, Elie Georges, Christian Gibson, Vik Goel, Tarun Gogineni, Gabriel Goh, Rapha Gontijo-Lopes, Jonathan Gordon, Morgan Grafstein, Scott Gray, Ryan Greene, Joshua Gross, Shixiang Shane Gu, Yufei Guo, Chris Hallacy, Jesse Han, Jeff Harris, Yuchen He, Mike Heaton, Johannes Heidecke, Chris Hesse, Alan Hickey, Wade Hickey, Peter Hoeschele, Brandon Houghton, Kenny Hsu, Shengli Hu, Xin Hu, Joost Huizinga, Shantanu Jain, Shawn Jain, Joanne Jang, Angela Jiang, Roger Jiang, Haozhun Jin, Denny Jin, Shino Jomoto, Billie Jonn, Heewoo Jun, Tomer Kaftan, Łukasz Kaiser, Ali Kamali, Ingmar Kanitscheider, Nitish Shirish Keskar, Tabarak Khan, Logan Kilpatrick, Jong Wook Kim, Christina Kim, Yongjik Kim, Jan Hendrik Kirchner, Jamie Kiros, Matt Knight, Daniel Kokotajlo, Łukasz Kondraciuk, Andrew Kondrich, Aris Konstantinidis, Kyle Kosic, Gretchen Krueger, Vishal Kuo, Michael Lampe, Ikai Lan, Teddy Lee, Jan Leike, Jade Leung, Daniel Levy, Chak Ming Li, Rachel Lim, Molly Lin, Stephanie Lin, Mateusz Litwin, Theresa Lopez, Ryan Lowe, Patricia Lue, Anna Makanju, Kim Malfacini, Sam Manning, Todor Markov, Yaniv Markovski, Bianca Martin, Katie Mayer, Andrew Mayne, Bob McGrew, Scott Mayer McKinney, Christine McLeavey, Paul McMillan, Jake McNeil, David Medina, Aalok Mehta, Jacob Menick, Luke Metz, Andrey Mishchenko, Pamela Mishkin, Vinnie Monaco, Evan Morikawa, Daniel Mossing, Tong Mu, Mira Murati, Oleg Murk, David Mély, Ashvin Nair, Reiichiro Nakano, Rajeev Nayak, Arvind Neelakantan, Richard Ngo, Hyeonwoo Noh, Long Ouyang, Cullen O'Keefe, Jakub Pachocki, Alex Paino, Joe Palermo, Ashley Pantuliano, Giambattista Parascandolo, Joel Parish, Emy Parparita, Alex Passos, Mikhail Pavlov, Andrew Peng, Adam Perelman, Filipe de Avila Belbute Peres, Michael Petrov, Henrique Ponde de Oliveira Pinto, Michael, Pokorny, Michelle Pokrass, Vitchyr H. Pong, Tolly Powell, Alethea Power, Boris Power, Elizabeth Proehl, Raul Puri, Alec Radford, Jack Rae, Aditya Ramesh, Cameron Raymond, Francis Real, Kendra Rimbach, Carl Ross, Bob Rotsted, Henri Roussez, Nick Ryder, Mario Saltarelli, Ted Sanders, Shibani Santurkar, Girish Sastry, Heather Schmidt, David Schnurr, John Schulman, Daniel Selsam, Kyla Sheppard, Toki Sherbakov, Jessica Shieh, Sarah Shoker, Pranav Shyam, Szymon Sidor, Eric Sigler, Maddie Simens, Jordan Sitkin, Katarina Slama, Ian Sohl, Benjamin Sokolowsky, Yang Song, Natalie Staudacher, Felipe Petroski Such, Natalie Summers, Ilya Sutskever, Jie Tang, Nikolas Tezak, Madeleine B. Thompson, Phil Tillet, Amin Tootoonchian, Elizabeth Tseng, Preston Tuggle, Nick Turley, Jerry Tworek, Juan Felipe Cerón Uribe, Andrea Vallone, Arun Vijayvergiya, Chelsea Voss, Carroll Wainwright, Justin Jay Wang, Alvin Wang, Ben Wang, Jonathan Ward, Jason Wei, CJ Weinmann, Akila Welihinda, Peter Welinder, Jiayi Weng, Lilian Weng, Matt Wiethoff, Dave Willner, Clemens Winter, Samuel Wolrich, Hannah Wong, Lauren Workman, Sherwin Wu, Jeff Wu, Michael Wu, Kai Xiao, Tao Xu, Sarah Yoo, Kevin Yu, Qiming Yuan, Wojciech Zaremba, Rowan Zellers, Chong Zhang, Marvin Zhang, Shengjia Zhao, Tianhao Zheng, Juntang Zhuang, William Zhuk, Barret Zoph
- Baichuan 2: Open Large-scale Language Models ↗
All 55 authors
Yang, Aiyuan, Xiao, Bin, Wang, Bingning, Zhang, Borong, Bian, Ce, Yin, Chao, Lv, Chenxu, Pan, Da, Wang, Dian, Yan, Dong, Yang, Fan, Deng, Fei, Wang, Feng, Liu, Feng, Ai, Guangwei, Dong, Guosheng, Zhao, Haizhou, Xu, Hang, Sun, Haoze, Zhang, Hongda, Liu, Hui, Ji, Jiaming, Xie, Jian, Dai, JunTao, Fang, Kun, Su, Lei, Song, Liang, Liu, Lifeng, Ru, Liyun, Ma, Luyao, Wang, Mang, Liu, Mickel, Lin, MingAn, Nie, Nuolan, Guo, Peidong, Sun, Ruiyang, Zhang, Tao, Li, Tianpeng, Li, Tianyu, Cheng, Wei, Chen, Weipeng, Zeng, Xiangrong, Wang, Xiaochuan, Chen, Xiaoxi, Men, Xin, Yu, Xin, Pan, Xuehai, Shen, Yanjun, Wang, Yiding, Li, Yiyu, Jiang, Youxin, Gao, Yuchen, Zhang, Yupeng, Zhou, Zenan, Wu, Zhiying
- Yi: Open Foundation Models by 01.AI ↗
All 33 authors
AI, 01., :, Young, Alex, Chen, Bei, Li, Chao, Huang, Chengen, Zhang, Ge, Zhang, Guanwei, Wang, Guoyin, Li, Heng, Zhu, Jiangcheng, Chen, Jianqun, Chang, Jing, Yu, Kaidong, Liu, Peng, Liu, Qiang, Yue, Shawn, Yang, Senbin, Yang, Shiming, Xie, Wen, Huang, Wenhao, Hu, Xiaohui, Ren, Xiaoyi, Niu, Xinyao, Nie, Pengcheng, Li, Yanpeng, Xu, Yuchi, Liu, Yudong, Wang, Yue, Cai, Yuxuan, Gu, Zhenyu, Liu, Zhiyuan, Dai, Zonghong
- Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena — published result table ↗
All 13 authors
Zheng, Lianmin, Chiang, Wei-Lin, Sheng, Ying, Zhuang, Siyuan, Wu, Zhanghao, Zhuang, Yonghao, Lin, Zi, Li, Zhuohan, Li, Dacheng, Xing, Eric P., Zhang, Hao, Gonzalez, Joseph E., Stoica, Ion
- CMMLU: Measuring massive multitask language understanding in Chinese — published result table ↗
All 8 authors
Li, Haonan, Zhang, Yixuan, Koto, Fajri, Yang, Yifei, Zhao, Hai, Gong, Yeyun, Duan, Nan, Baldwin, Timothy
- COLLIE: Systematic Construction of Constrained Text Generation Tasks — published result table ↗
- SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models — published result table ↗
All 10 authors
Wang, Xiaoxuan, Hu, Ziniu, Lu, Pan, Zhu, Yanqiao, Zhang, Jieyu, Subramaniam, Satyen, Loomba, Arjun R., Zhang, Shichang, Sun, Yizhou, Wang, Wei
- WebArena: A Realistic Web Environment for Building Autonomous Agents — published result table ↗
All 12 authors
Zhou, Shuyan, Xu, Frank F., Zhu, Hao, Zhou, Xuhui, Lo, Robert, Sridhar, Abishek, Cheng, Xianyi, Ou, Tianyue, Bisk, Yonatan, Fried, Daniel, Alon, Uri, Neubig, Graham
- XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models — published result table ↗
- FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation — published result table ↗
All 11 authors
Vu, Tu, Iyyer, Mohit, Wang, Xuezhi, Constant, Noah, Wei, Jerry, Wei, Jason, Tar, Chris, Sung, Yun-Hsuan, Zhou, Denny, Le, Quoc, Luong, Thang
- Towards Understanding Sycophancy in Language Models — published result table ↗
All 19 authors
Sharma, Mrinank, Tong, Meg, Korbak, Tomasz, Duvenaud, David, Askell, Amanda, Bowman, Samuel R., Cheng, Newton, Durmus, Esin, Hatfield-Dodds, Zac, Johnston, Scott R., Kravec, Shauna, Maxwell, Timothy, McCandlish, Sam, Ndousse, Kamal, Rausch, Oliver, Schiefer, Nicholas, Yan, Da, Zhang, Miranda, Perez, Ethan
- MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning — published result table ↗
- BizBench: A Quantitative Reasoning Benchmark for Business and Finance — published result table ↗
- GAIA: a benchmark for General AI Assistants — published result table ↗
- Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models — published result table ↗
All 7 authors
Jingwei Yi, Yueqi Xie, Bin Zhu, Emre Kiciman, Guangzhong Sun, Xing Xie, Fangzhao Wu
- VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks — published result table ↗
All 10 authors
Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Chong Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Ruslan Salakhutdinov, Daniel Fried
- CriticBench: Benchmarking LLMs for Critique-Correct Reasoning — published result table ↗
- GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers — published result table ↗
- MathScale: Scaling Instruction Tuning for Mathematical Reasoning — published result table ↗
- JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models — published result table ↗
All 12 authors
Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramer, Hamed Hassani, Eric Wong
- IndicGenBench: A Multilingual Benchmark to Evaluate Generation Capabilities of LLMs on Indic Languages — published result table ↗
- Large Language Models in the Clinic: A Comprehensive Benchmark — published result table ↗
All 19 authors
Liu, Fenglin, Li, Zheng, Zhou, Hongjian, Yin, Qingyu, Yang, Jingfeng, Tang, Xianfeng, Luo, Chen, Zeng, Ming, Jiang, Haoming, Gao, Yifan, Nigam, Priyanka, Nag, Sreyashi, Yin, Bing, Hua, Yining, Zhou, Xuan, Rohanian, Omid, Thakur, Anshul, Clifton, Lei, Clifton, David A.
- WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia — published result table ↗
All 8 authors
Hou, Yufang, Pascale, Alessandra, Carnerero-Cano, Javier, Tchrakian, Tigran, Marinescu, Radu, Daly, Elizabeth, Padhi, Inkit, Sattigeri, Prasanna
- MathOdyssey: Benchmarking Mathematical Problem-Solving Skills in Large Language Models Using Odyssey Math Data — published result table ↗
- WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs — published result table ↗
All 8 authors
Seungju Han, Kavel Rao, Allyson Ettinger, Liwei Jiang, Bill Yuchen Lin, Nathan Lambert, Yejin Choi, Nouha Dziri
- PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition — published result table ↗
All 8 authors
George Tsoukalas, Jasper Lee, John Jennings, Jimmy Xin, Michelle Ding, Michael Jennings, Amitayush Thakur, Swarat Chaudhuri
- The Art of Saying No: Contextual Noncompliance in Language Models — published result table ↗
All 14 authors
Brahman, Faeze, Kumar, Sachin, Balachandran, Vidhisha, Dasigi, Pradeep, Pyatkin, Valentina, Ravichander, Abhilasha, Wiegreffe, Sarah, Dziri, Nouha, Chandu, Khyathi, Hessel, Jack, Tsvetkov, Yulia, Smith, Noah A., Choi, Yejin, Hajishirzi, Hannaneh
- The Llama 3 Herd of Models — published result table ↗
All 561 authors
Grattafiori, Aaron, Dubey, Abhimanyu, Jauhri, Abhinav, Pandey, Abhinav, Kadian, Abhishek, Al-Dahle, Ahmad, Letman, Aiesha, Mathur, Akhil, Schelten, Alan, Vaughan, Alex, Yang, Amy, Fan, Angela, Goyal, Anirudh, Hartshorn, Anthony, Yang, Aobo, Mitra, Archi, Sravankumar, Archie, Korenev, Artem, Hinsvark, Arthur, Rao, Arun, Zhang, Aston, Rodriguez, Aurelien, Gregerson, Austen, Spataru, Ava, Roziere, Baptiste, Biron, Bethany, Tang, Binh, Chern, Bobbie, Caucheteux, Charlotte, Nayak, Chaya, Bi, Chloe, Marra, Chris, McConnell, Chris, Keller, Christian, Touret, Christophe, Wu, Chunyang, Wong, Corinne, Ferrer, Cristian Canton, Nikolaidis, Cyrus, Allonsius, Damien, Song, Daniel, Pintz, Danielle, Livshits, Danny, Wyatt, Danny, Esiobu, David, Choudhary, Dhruv, Mahajan, Dhruv, Garcia-Olano, Diego, Perino, Diego, Hupkes, Dieuwke, Lakomkin, Egor, AlBadawy, Ehab, Lobanova, Elina, Dinan, Emily, Smith, Eric Michael, Radenovic, Filip, Guzmán, Francisco, Zhang, Frank, Synnaeve, Gabriel, Lee, Gabrielle, Anderson, Georgia Lewis, Thattai, Govind, Nail, Graeme, Mialon, Gregoire, Pang, Guan, Cucurell, Guillem, Nguyen, Hailey, Korevaar, Hannah, Xu, Hu, Touvron, Hugo, Zarov, Iliyan, Ibarra, Imanol Arrieta, Kloumann, Isabel, Misra, Ishan, Evtimov, Ivan, Zhang, Jack, Copet, Jade, Lee, Jaewon, Geffert, Jan, Vranes, Jana, Park, Jason, Mahadeokar, Jay, Shah, Jeet, van der Linde, Jelmer, Billock, Jennifer, Hong, Jenny, Lee, Jenya, Fu, Jeremy, Chi, Jianfeng, Huang, Jianyu, Liu, Jiawen, Wang, Jie, Yu, Jiecao, Bitton, Joanna, Spisak, Joe, Park, Jongsoo, Rocca, Joseph, Johnstun, Joshua, Saxe, Joshua, Jia, Junteng, Alwala, Kalyan Vasuden, Prasad, Karthik, Upasani, Kartikeya, Plawiak, Kate, Li, Ke, Heafield, Kenneth, Stone, Kevin, El-Arini, Khalid, Iyer, Krithika, Malik, Kshitiz, Chiu, Kuenley, Bhalla, Kunal, Lakhotia, Kushal, Rantala-Yeary, Lauren, van der Maaten, Laurens, Chen, Lawrence, Tan, Liang, Jenkins, Liz, Martin, Louis, Madaan, Lovish, Malo, Lubo, Blecher, Lukas, Landzaat, Lukas, de Oliveira, Luke, Muzzi, Madeline, Pasupuleti, Mahesh, Singh, Mannat, Paluri, Manohar, Kardas, Marcin, Tsimpoukelli, Maria, Oldham, Mathew, Rita, Mathieu, Pavlova, Maya, Kambadur, Melanie, Lewis, Mike, Si, Min, Singh, Mitesh Kumar, Hassan, Mona, Goyal, Naman, Torabi, Narjes, Bashlykov, Nikolay, Bogoychev, Nikolay, Chatterji, Niladri, Zhang, Ning, Duchenne, Olivier, Çelebi, Onur, Alrassy, Patrick, Zhang, Pengchuan, Li, Pengwei, Vasic, Petar, Weng, Peter, Bhargava, Prajjwal, Dubal, Pratik, Krishnan, Praveen, Koura, Punit Singh, Xu, Puxin, He, Qing, Dong, Qingxiao, Srinivasan, Ragavan, Ganapathy, Raj, Calderer, Ramon, Cabral, Ricardo Silveira, Stojnic, Robert, Raileanu, Roberta, Maheswari, Rohan, Girdhar, Rohit, Patel, Rohit, Sauvestre, Romain, Polidoro, Ronnie, Sumbaly, Roshan, Taylor, Ross, Silva, Ruan, Hou, Rui, Wang, Rui, Hosseini, Saghar, Chennabasappa, Sahana, Singh, Sanjay, Bell, Sean, Kim, Seohyun Sonia, Edunov, Sergey, Nie, Shaoliang, Narang, Sharan, Raparthy, Sharath, Shen, Sheng, Wan, Shengye, Bhosale, Shruti, Zhang, Shun, Vandenhende, Simon, Batra, Soumya, Whitman, Spencer, Sootla, Sten, Collot, Stephane, Gururangan, Suchin, Borodinsky, Sydney, Herman, Tamar, Fowler, Tara, Sheasha, Tarek, Georgiou, Thomas, Scialom, Thomas, Speckbacher, Tobias, Mihaylov, Todor, Xiao, Tong, Karn, Ujjwal, Goswami, Vedanuj, Gupta, Vibhor, Ramanathan, Vignesh, Kerkez, Viktor, Gonguet, Vincent, Do, Virginie, Vogeti, Vish, Albiero, Vítor, Petrovic, Vladan, Chu, Weiwei, Xiong, Wenhan, Fu, Wenyin, Meers, Whitney, Martinet, Xavier, Wang, Xiaodong, Wang, Xiaofang, Tan, Xiaoqing Ellen, Xia, Xide, Xie, Xinfeng, Jia, Xuchao, Wang, Xuewei, Goldschlag, Yaelle, Gaur, Yashesh, Babaei, Yasmine, Wen, Yi, Song, Yiwen, Zhang, Yuchen, Li, Yue, Mao, Yuning, Coudert, Zacharie Delpierre, Yan, Zheng, Chen, Zhengxing, Papakipos, Zoe, Singh, Aaditya, Srivastava, Aayushi, Jain, Abha, Kelsey, Adam, Shajnfeld, Adam, Gangidi, Adithya, Victoria, Adolfo, Goldstand, Ahuva, Menon, Ajay, Sharma, Ajay, Boesenberg, Alex, Baevski, Alexei, Feinstein, Allie, Kallet, Amanda, Sangani, Amit, Teo, Amos, Yunus, Anam, Lupu, Andrei, Alvarado, Andres, Caples, Andrew, Gu, Andrew, Ho, Andrew, Poulton, Andrew, Ryan, Andrew, Ramchandani, Ankit, Dong, Annie, Franco, Annie, Goyal, Anuj, Saraf, Aparajita, Chowdhury, Arkabandhu, Gabriel, Ashley, Bharambe, Ashwin, Eisenman, Assaf, Yazdan, Azadeh, James, Beau, Maurer, Ben, Leonhardi, Benjamin, Huang, Bernie, Loyd, Beth, De Paola, Beto, Paranjape, Bhargavi, Liu, Bing, Wu, Bo, Ni, Boyu, Hancock, Braden, Wasti, Bram, Spence, Brandon, Stojkovic, Brani, Gamido, Brian, Montalvo, Britt, Parker, Carl, Burton, Carly, Mejia, Catalina, Liu, Ce, Wang, Changhan, Kim, Changkyu, Zhou, Chao, Hu, Chester, Chu, Ching-Hsiang, Cai, Chris, Tindal, Chris, Feichtenhofer, Christoph, Gao, Cynthia, Civin, Damon, Beaty, Dana, Kreymer, Daniel, Li, Daniel, Adkins, David, Xu, David, Testuggine, Davide, David, Delia, Parikh, Devi, Liskovich, Diana, Foss, Didem, Wang, Dingkang, Le, Duc, Holland, Dustin, Dowling, Edward, Jamil, Eissa, Montgomery, Elaine, Presani, Eleonora, Hahn, Emily, Wood, Emily, Le, Eric-Tuan, Brinkman, Erik, Arcaute, Esteban, Dunbar, Evan, Smothers, Evan, Sun, Fei, Kreuk, Felix, Tian, Feng, Kokkinos, Filippos, Ozgenel, Firat, Caggioni, Francesco, Kanayet, Frank, Seide, Frank, Florez, Gabriela Medina, Schwarz, Gabriella, Badeer, Gada, Swee, Georgia, Halpern, Gil, Herman, Grant, Sizov, Grigory, Guangyi, Zhang, Lakshminarayanan, Guna, Inan, Hakan, Shojanazeri, Hamid, Zou, Han, Wang, Hannah, Zha, Hanwen, Habeeb, Haroun, Rudolph, Harrison, Suk, Helen, Aspegren, Henry, Goldman, Hunter, Zhan, Hongyuan, Damlaj, Ibrahim, Molybog, Igor, Tufanov, Igor, Leontiadis, Ilias, Veliche, Irina-Elena, Gat, Itai, Weissman, Jake, Geboski, James, Kohli, James, Lam, Janice, Asher, Japhet, Gaya, Jean-Baptiste, Marcus, Jeff, Tang, Jeff, Chan, Jennifer, Zhen, Jenny, Reizenstein, Jeremy, Teboul, Jeremy, Zhong, Jessica, Jin, Jian, Yang, Jingyi, Cummings, Joe, Carvill, Jon, Shepard, Jon, McPhie, Jonathan, Torres, Jonathan, Ginsburg, Josh, Wang, Junjie, Wu, Kai, U, Kam Hou, Saxena, Karan, Khandelwal, Kartikay, Zand, Katayoun, Matosich, Kathy, Veeraraghavan, Kaushik, Michelena, Kelly, Li, Keqian, Jagadeesh, Kiran, Huang, Kun, Chawla, Kunal, Huang, Kyle, Chen, Lailin, Garg, Lakshya, A, Lavender, Silva, Leandro, Bell, Lee, Zhang, Lei, Guo, Liangpeng, Yu, Licheng, Moshkovich, Liron, Wehrstedt, Luca, Khabsa, Madian, Avalani, Manav, Bhatt, Manish, Mankus, Martynas, Hasson, Matan, Lennie, Matthew, Reso, Matthias, Groshev, Maxim, Naumov, Maxim, Lathi, Maya, Keneally, Meghan, Liu, Miao, Seltzer, Michael L., Valko, Michal, Restrepo, Michelle, Patel, Mihir, Vyatskov, Mik, Samvelyan, Mikayel, Clark, Mike, Macey, Mike, Wang, Mike, Hermoso, Miquel Jubert, Metanat, Mo, Rastegari, Mohammad, Bansal, Munish, Santhanam, Nandhini, Parks, Natascha, White, Natasha, Bawa, Navyata, Singhal, Nayan, Egebo, Nick, Usunier, Nicolas, Mehta, Nikhil, Laptev, Nikolay Pavlovich, Dong, Ning, Cheng, Norman, Chernoguz, Oleg, Hart, Olivia, Salpekar, Omkar, Kalinli, Ozlem, Kent, Parkin, Parekh, Parth, Saab, Paul, Balaji, Pavan, Rittner, Pedro, Bontrager, Philip, Roux, Pierre, Dollar, Piotr, Zvyagina, Polina, Ratanchandani, Prashant, Yuvraj, Pritish, Liang, Qian, Alao, Rachad, Rodriguez, Rachel, Ayub, Rafi, Murthy, Raghotham, Nayani, Raghu, Mitra, Rahul, Parthasarathy, Rangaprabhu, Li, Raymond, Hogan, Rebekkah, Battey, Robin, Wang, Rocky, Howes, Russ, Rinott, Ruty, Mehta, Sachin, Siby, Sachin, Bondu, Sai Jayesh, Datta, Samyak, Chugh, Sara, Hunt, Sara, Dhillon, Sargun, Sidorov, Sasha, Pan, Satadru, Mahajan, Saurabh, Verma, Saurabh, Yamamoto, Seiji, Ramaswamy, Sharadh, Lindsay, Shaun, Lindsay, Shaun, Feng, Sheng, Lin, Shenghao, Zha, Shengxin Cindy, Patil, Shishir, Shankar, Shiva, Zhang, Shuqiang, Zhang, Shuqiang, Wang, Sinong, Agarwal, Sneha, Sajuyigbe, Soji, Chintala, Soumith, Max, Stephanie, Chen, Stephen, Kehoe, Steve, Satterfield, Steve, Govindaprasad, Sudarshan, Gupta, Sumit, Deng, Summer, Cho, Sungmin, Virk, Sunny, Subramanian, Suraj, Choudhury, Sy, Goldman, Sydney, Remez, Tal, Glaser, Tamar, Best, Tamara, Koehler, Thilo, Robinson, Thomas, Li, Tianhe, Zhang, Tianjun, Matthews, Tim, Chou, Timothy, Shaked, Tzook, Vontimitta, Varun, Ajayi, Victoria, Montanez, Victoria, Mohan, Vijai, Kumar, Vinay Satish, Mangla, Vishal, Ionescu, Vlad, Poenaru, Vlad, Mihailescu, Vlad Tiberiu, Ivanov, Vladimir, Li, Wei, Wang, Wenchen, Jiang, Wenwen, Bouaziz, Wes, Constable, Will, Tang, Xiaocheng, Wu, Xiaojian, Wang, Xiaolan, Wu, Xilun, Gao, Xinbo, Kleinman, Yaniv, Chen, Yanjun, Hu, Ye, Jia, Ye, Qi, Ye, Li, Yenda, Zhang, Yilin, Zhang, Ying, Adi, Yossi, Nam, Youngjin, Yu, Wang, Zhao, Yu, Hao, Yuchen, Qian, Yundi, Li, Yunlu, He, Yuzi, Rait, Zach, DeVito, Zachary, Rosnbrick, Zef, Wen, Zhaoduo, Yang, Zhenyu, Zhao, Zhiwei, Ma, Zhiyu
- HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly — published result table ↗
All 8 authors
Howard Yen, Tianyu Gao, Minmin Hou, Ke Ding, Daniel Fleischer, Peter Izsak, Moshe Wasserblat, Danqi Chen
- Putnam-AXIOM: A Functional and Static Benchmark for Measuring Higher Level Mathematical Reasoning in LLMs — published result table ↗
All 8 authors
Gulati, Aryan, Miranda, Brando, Chen, Eric, Xia, Emily, Fronsdal, Kai, Dumont, Bruno, Obbad, Elyas, Koyejo, Sanmi
- Are large language models superhuman chemists? — published result table ↗
All 35 authors
Adrian Mirza, Nawaf Alampara, Sreekanth Kunchapu, Martiño Ríos-García, Benedict Emoekabu, Aswanth Krishnan, Tanya Gupta, Mara Schilling-Wilhelmi, Macjonathan Okereke, Anagha Aneesh, Amir Mohammad Elahi, Mehrdad Asgari, Juliane Eberhardt, Hani M. Elbeheiry, María Victoria Gil, Maximilian Greiner, Caroline T. Holick, Christina Glaubitz, Tim Hoffmann, Abdelrahman Ibrahim, Lea C. Klepsch, Yannik Köster, Fabian Alexander Kreth, Jakob Meyer, Santiago Miret, Jan Matthias Peschel, Michael Ringleb, Nicole Roesner, Johanna Schreiber, Ulrich S. Schubert, Leanne M. Stafast, Dinga Wonanke, Michael Pieler, Philippe Schwaller, Kevin Maik Jablonka
- DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data — published result table ↗
- Do-Not-Answer: Evaluating Safeguards in LLMs ↗
- OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems — published result table ↗
All 14 authors
Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Leng Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi, Zhiyuan Liu, Maosong Sun
- C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models — published result table ↗
All 13 authors
Yuzhen Huang, Yuzhuo Bai, Zhihao Zhu, Junlei Zhang, Jinghan Zhang, Tangjun Su, Junteng Liu, Chuancheng Lv, Yikai Zhang, Jiayi Lei, Yao Fu, Maosong Sun, Junxian He
- PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change — published result table ↗
- LawBench: Benchmarking Legal Knowledge of Large Language Models — published result table ↗
All 9 authors
Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Songyang Zhang, Kai Chen, Zongwen Shen, Jidong Ge
- FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation — published result table ↗
All 9 authors
Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Wei Koh, Mohit Iyyer, Luke Zettlemoyer, Hannaneh Hajishirzi
- AlpacaEval: An Automatic Evaluator of Instruction-following Models ↗
All 8 authors
Xuechen Li, Tianyi Zhang, Yann Dubois, Rohan Taori, Ishaan Gulrajani, Carlos Guestrin, Percy Liang, Tatsunori B. Hashimoto
Names used in source tables
GPT 4* / - · GPT-4 · GPT4 · gpt4