GPT-3.5 Turbo · MMLU · EM · % ↗
OpenAI / MODEL EVIDENCE
GPT-3.5 Turbo
22 benchmarks · 74 observations · 19 sources
Scores at a glance
22 benchmarks · large dot: GPT-3.5 Turbo; small dots: closest models where reported
GPT-3.5-Turbo · GSM-Plus / GSM-Symbolic · GSM-Plus accuracy · Original robustness evaluation ↗
GPT-3.5-Turbo · CollegeMath · CollegeMath accuracy · MathScale paper · MwpBench evaluation ↗
GPT-3.5 Turbo · MathOdyssey · Overall accuracy · original model comparison ↗
GPT-3.5-Turbo · AGIEval · Accuracy · English · few-shot ↗
5 more reported results
49.5% · GPT-3.5-Turbo · AGIEval · Accuracy · Chinese · few-shot · AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models ↗
47.2% · GPT-3.5-Turbo · AGIEval · Accuracy · All · few-shot · AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models ↗
54.1% · GPT-3.5-Turbo · AGIEval · Accuracy · English · zero-shot · AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models ↗
45% · GPT-3.5-Turbo · AGIEval · Accuracy · Chinese · zero-shot · AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models ↗
46% · GPT-3.5-Turbo · AGIEval · Accuracy · All · zero-shot · AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models ↗
GPT-3.5-turbo · LawBench · Task average · Zero-shot ↗
1 more reported result
44.52% · GPT-3.5-turbo · LawBench · Task average · One-shot · LawBench: Benchmarking Legal Knowledge of Large Language Models ↗
GPT-3.5-Turbo · BIPIA & InjecAgent (Indirect Prompt Injection) · BIPIA overall attack success · black-box defense comparison ↗
2 more reported results
18.84% · GPT-3.5-Turbo · 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 ↗
18.43% · GPT-3.5-Turbo · 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-3.5 Turbo · AgentHarm & AgentDojo · AgentDojo targeted attack success · agent comparison ↗
1 more reported result
33.86% · GPT-3.5 Turbo · AgentHarm & AgentDojo · AgentDojo benign utility · agent comparison · AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents ↗
GPT-3.5 Turbo · HumanEval+ · Pass@1 · Llama 3 report · post-trained models ↗
GPT-3.5 Turbo · HumanEval · Pass@1 · Llama 3 report · post-trained models ↗
gpt-3.5-turbo · MACHIAVELLI · Normalized reward · agent comparison ↗
3 more reported results
100 points · gpt-3.5-turbo · MACHIAVELLI · Normalized power · agent comparison · Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark ↗
102 points · gpt-3.5-turbo · MACHIAVELLI · Normalized disutility · agent comparison · Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark ↗
90 points · gpt-3.5-turbo · MACHIAVELLI · Normalized ethical violations · agent comparison · Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark ↗
gpt-3.5-turbo · τ-Bench · Retail · pass^1 · Original tau-bench · function-calling agents ↗
2 more reported results
10.8% · gpt-3.5-turbo · τ-Bench · Airline · pass^1 · Original tau-bench · function-calling agents · $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains ↗
15.4% · gpt-3.5-turbo · τ-Bench · Domain average · pass^1 · Original tau-bench · function-calling agents · $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains ↗
GPT-3.5 Turbo · HealthBench · 16-run mean ↗
GPT-3.5 Turbo · HealthBench Consensus · Overall criterion score · original comparison ↗
GPT-3.5 Turbo · ChemBench · Overall accuracy · published model comparison ↗
1 more reported result
44% · GPT-3.5 Turbo · ChemBench · Human-evaluation subset · accuracy · published model comparison · Are large language models superhuman chemists? ↗
GPT-3.5 Turbo · HealthBench Hard · Weighted rubric score · original model comparison ↗
GPT-3.5-turbo · LoCoMo (Long-Term Conversational Memory) · Overall answer F1 · base and long-context comparison ↗
GPT-3.5-Turbo · MixEval & MixEval-Hard · accuracy · original comparison ↗
1 more reported result
79.7% · GPT-3.5-Turbo · MixEval & MixEval-Hard · accuracy · original comparison · MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures ↗
GPT-3.5-Turbo · SciBench · Textbook overall accuracy · prompting and tool conditions ↗
3 more reported results
12.17% · GPT-3.5-Turbo · SciBench · Textbook overall accuracy · prompting and tool conditions · SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models ↗
11.99% · GPT-3.5-Turbo · SciBench · Textbook overall accuracy · prompting and tool conditions · SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models ↗
19.91% · GPT-3.5-Turbo · SciBench · Textbook overall accuracy · prompting and tool conditions · SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models ↗
GPT-3.5-Turbo · CoCoNot (Contextual Non-Compliance) · No system prompt · Incomplete · compliance rate · reported model comparison ↗
11 more reported results
21% · GPT-3.5-Turbo · CoCoNot (Contextual Non-Compliance) · No system prompt · Unsupported · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
16.9% · GPT-3.5-Turbo · CoCoNot (Contextual Non-Compliance) · No system prompt · Indeterminate · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
8.1% · GPT-3.5-Turbo · CoCoNot (Contextual Non-Compliance) · No system prompt · Safety · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
13.4% · GPT-3.5-Turbo · CoCoNot (Contextual Non-Compliance) · No system prompt · Humanizing · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
95.3% · GPT-3.5-Turbo · CoCoNot (Contextual Non-Compliance) · No system prompt · Contrast set · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
25.3% · GPT-3.5-Turbo · CoCoNot (Contextual Non-Compliance) · With system prompt · Incomplete · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
12.7% · GPT-3.5-Turbo · CoCoNot (Contextual Non-Compliance) · With system prompt · Unsupported · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
9.9% · GPT-3.5-Turbo · CoCoNot (Contextual Non-Compliance) · With system prompt · Indeterminate · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
0.8% · GPT-3.5-Turbo · CoCoNot (Contextual Non-Compliance) · With system prompt · Safety · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
4.9% · GPT-3.5-Turbo · CoCoNot (Contextual Non-Compliance) · With system prompt · Humanizing · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
90.8% · GPT-3.5-Turbo · CoCoNot (Contextual Non-Compliance) · With system prompt · Contrast set · compliance rate · reported model comparison · The Art of Saying No: Contextual Noncompliance in Language Models ↗
GPT-3.5 Turbo · DarkBench · DarkScore · six-category mean · publisher website comparison ↗
6 more reported results
66% · GPT-3.5 Turbo · DarkBench · Anthropomorphization · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗
31% · GPT-3.5 Turbo · DarkBench · Brand bias · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗
85% · GPT-3.5 Turbo · DarkBench · Harmful generation · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗
62% · GPT-3.5 Turbo · DarkBench · Sneaking · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗
26% · GPT-3.5 Turbo · DarkBench · Sycophancy · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗
95% · GPT-3.5 Turbo · DarkBench · User retention · publisher website comparison · DarkBench: Benchmarking Dark Patterns in Large Language Models — publisher leaderboard data ↗
GPT-3.5-turbo · ClinicBench · MedQA · Accuracy · zero-shot main comparison ↗
16 more reported results
59.4% · GPT-3.5-turbo · ClinicBench · MedMCQA · Accuracy · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
73.5% · GPT-3.5-turbo · ClinicBench · MMLU · Accuracy · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
70.2% · GPT-3.5-turbo · ClinicBench · PubMedQA · Accuracy · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
81.1% · GPT-3.5-turbo · ClinicBench · Referral QA · Accuracy · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
7.3 F1 points · GPT-3.5-turbo · ClinicBench · Treat Recom. · F1 · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
14.1 ROUGE-L points · GPT-3.5-turbo · ClinicBench · MIMIC · ROUGE-L · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
10.3 ROUGE-L points · GPT-3.5-turbo · ClinicBench · IU-Xray · ROUGE-L · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
10.5 ROUGE-L points · GPT-3.5-turbo · ClinicBench · Hospitaliz. Summari. · ROUGE-L · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
9.2 ROUGE-L points · GPT-3.5-turbo · ClinicBench · Patient Education · ROUGE-L · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
52.3 F1 entity-level points · GPT-3.5-turbo · ClinicBench · BC5 · F1 entity-level · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
46.1 F1 entity-level points · GPT-3.5-turbo · ClinicBench · NCBI · F1 entity-level · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
49.3 Micro F1 points · GPT-3.5-turbo · ClinicBench · DDI · Micro F1 · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
50.8 Micro F1 points · GPT-3.5-turbo · ClinicBench · GAD · Micro F1 · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
66.4 Micro F1 points · GPT-3.5-turbo · ClinicBench · HoC · Micro F1 · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
57.3% · GPT-3.5-turbo · ClinicBench · Pharma. QA · Accuracy · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
47% · GPT-3.5-turbo · ClinicBench · Drug Inter. · Accuracy · zero-shot main comparison · Large Language Models in the Clinic: A Comprehensive Benchmark ↗
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.
- 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
- Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark — published result table ↗
All 10 authors
Alexander Pan, Jun Shern Chan, Andy Zou, Nathaniel Li, Steven Basart, Thomas Woodside, Jonathan Ng, Hanlin Zhang, Scott Emmons, Dan Hendrycks
- 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
- 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
- Evaluating Very Long-Term Conversational Memory of LLM Agents — 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 ↗
- 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.
- MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures — published result table ↗
All 8 authors
Jinjie Ni, Fuzhao Xue, Xiang Yue, Yuntian Deng, Mahir Shah, Kabir Jain, Graham Neubig, Yang You
- $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains — published result table ↗
- AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents — published result table ↗
- MathOdyssey: Benchmarking Mathematical Problem-Solving Skills in Large Language Models Using Odyssey Math Data — published result table ↗
- 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
- HealthBench: Evaluating Large Language Models Towards Improved Human Health — published result table ↗
All 12 authors
Arora, Rahul K., Wei, Jason, Hicks, Rebecca Soskin, Bowman, Preston, Quiñonero-Candela, Joaquin, Tsimpourlas, Foivos, Sharman, Michael, Shah, Meghan, Vallone, Andrea, Beutel, Alex, Heidecke, Johannes, Singhal, Karan
- 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 ↗
- 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
- AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models — published result table ↗
All 9 authors
Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, Nan Duan
Names used in source tables
GPT-3.5 Turbo · GPT-3.5-Turbo · GPT-3.5-turbo · gpt-3.5-turbo