llama-2-70b / chat · AgentBench & ToolBench · AgentBench overall · Original standard test set ↗
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Llama 2-70b / chat
10 benchmarks · 24 observations · 9 sources
Scores at a glance
10 benchmarks · large dot: Llama 2-70b / chat; small dots: closest models where reported
Llama2 70b Chat · MuSR (Multistep Soft Reasoning) · Accuracy · Murder mysteries · CoT+ ↗
2 more reported results
42.2% · Llama2 70b Chat · MuSR (Multistep Soft Reasoning) · Accuracy · Object placement · CoT+ · MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning ↗
44.8% · Llama2 70b Chat · MuSR (Multistep Soft Reasoning) · Accuracy · Team allocation · CoT+ · MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning ↗
LLaMa-2-70b chat · CriticBench · Critiquing · zero-shot answer-only ↗
2 more reported results
53.2% · LLaMa-2-70b chat · CriticBench · Critiquing · zero-shot CoT · CriticBench: Benchmarking LLMs for Critique-Correct Reasoning ↗
30.51% · LLaMa-2-70b chat · CriticBench · Correction · zero-shot CoT · CriticBench: Benchmarking LLMs for Critique-Correct Reasoning ↗
Llama-2-70b-chat · HarmBench · HarmBench harmful compliance · SORRY-Bench paper · cross-benchmark comparison ↗
Llama-2-70b-chat · StrongREJECT · StrongREJECT harmful compliance · SORRY-Bench paper · cross-benchmark comparison ↗
Llama-2-70B-chat · WildBench · WB-Score · reported comparison ↗
Llama-2-70B Chat · ChemBench · Overall accuracy · published model comparison ↗
1 more reported result
14% · Llama-2-70B Chat · ChemBench · Human-evaluation subset · accuracy · published model comparison · Are large language models superhuman chemists? ↗
Llama-2-70b-chat · InFoBench · Decomposed requirement following rate · GPT-4-1106 evaluator ↗
LLaMA-2-70B-Chat · MixEval & MixEval-Hard · accuracy · original comparison ↗
1 more reported result
74.6% · LLaMA-2-70B-Chat · MixEval & MixEval-Hard · accuracy · original comparison · MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures ↗
Llama-2-70b-chat · WikiContradict · WikiContradict HumanEval · Template 1 · All · fully correct · human annotation ↗
8 more reported results
0% · Llama-2-70b-chat · WikiContradict · WikiContradict HumanEval · Template 1 · Explicit · fully correct · human annotation · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
0% · Llama-2-70b-chat · WikiContradict · WikiContradict HumanEval · Template 1 · Implicit · fully correct · human annotation · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
4.2% · Llama-2-70b-chat · 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% · Llama-2-70b-chat · WikiContradict · WikiContradict HumanEval · Template 4 · Explicit · fully correct · human annotation · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
5.9% · Llama-2-70b-chat · WikiContradict · WikiContradict HumanEval · Template 4 · Implicit · fully correct · human annotation · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
22.9% · Llama-2-70b-chat · WikiContradict · WikiContradict HumanEval · Template 5 · All · fully correct · human annotation · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
26.7% · Llama-2-70b-chat · WikiContradict · WikiContradict HumanEval · Template 5 · Explicit · fully correct · human annotation · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
17.6% · Llama-2-70b-chat · WikiContradict · WikiContradict HumanEval · Template 5 · Implicit · fully correct · human annotation · WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia ↗
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.
- AgentBench: Evaluating LLMs as Agents — published result table ↗
All 22 authors
Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, Shudan Zhang, Xiang Deng, Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Sheng Shen, Tianjun Zhang, Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, Jie Tang
- MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning — published result table ↗
- InFoBench: Evaluating Instruction Following Ability in Large Language Models — published result table ↗
All 10 authors
Yiwei Qin, Kaiqiang Song, Yebowen Hu, Wenlin Yao, Sangwoo Cho, Xiaoyang Wang, Xuansheng Wu, Fei Liu, Pengfei Liu, Dong Yu
- CriticBench: Benchmarking LLMs for Critique-Correct Reasoning — published result table ↗
- WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild — published result table ↗
All 9 authors
Lin, Bill Yuchen, Deng, Yuntian, Chandu, Khyathi, Brahman, Faeze, Ravichander, Abhilasha, Pyatkin, Valentina, Dziri, Nouha, Bras, Ronan Le, Choi, Yejin
- 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
- 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
- SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal — published result table ↗
All 16 authors
Tinghao Xie, Xiangyu Qi, Yi Zeng, Yangsibo Huang, Udari Madhushani Sehwag, Kaixuan Huang, Luxi He, Boyi Wei, Dacheng Li, Ying Sheng, Ruoxi Jia, Bo Li, Kai Li, Danqi Chen, Peter Henderson, Prateek Mittal
- 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
Names used in source tables
LLaMA-2-70B-Chat · LLaMa-2-70b chat · Llama-2-70B Chat · Llama-2-70B-chat · Llama-2-70b-chat · Llama2 70b Chat · llama-2-70b / chat