README.md

May 21, 2026 · View on GitHub

LLMEval-1: Large-Scale Chinese LLM Evaluation (Phase I)

Note: For the Chinese version of this README, please refer to README_zh.md.

🔔 News

🏆 [2024-03-24] Our paper "LLMEval: A Preliminary Study on How to Evaluate Large Language Models" has been accepted at AAAI 2024.
📊 [2023-06-19] Evaluation results released: 2,186 users participated, generating 243,000+ annotations.
🎉 [2023-05-01] LLMEval-1 evaluation launched.

LLMEval-1 is the Phase I dataset of the LLMEval paper (AAAI 2024). It is a large-scale Chinese LLM evaluation covering 17 major categories with 453 questions, spanning factual QA, reading comprehension, framework generation, paragraph rewriting, summarization, mathematical problem-solving, reasoning, poetry generation, programming, and more.

Key Features

Generative evaluation — unlike multiple-choice benchmarks, models generate free-form answers
Five evaluation dimensions — Correctness, Fluency, Informativeness, Logic, and Harmlessness
Multi-annotator evaluation — combining onsite experts, crowd-sourcing workers, public volunteers, and GPT-4
2,186 participants contributed 243,337 manual annotations
57,511 GPT-4 automated evaluation results

🏆 Leaderboard

Model	Overall	Rank	Accuracy	Info.	Fluency	Logic	Safety	Pairwise	Rank
GPT-4	2.833 (2.917)	1 (1)	2.709 (2.803)	2.817 (2.882)	2.870 (3.000)	2.832 (2.901)	2.937 (3.000)	0.701 (0.894)	1 (1)
GPT-3.5	2.789 (2.878)	2 (2)	2.616 (2.717)	2.742 (2.807)	2.850 (3.000)	2.785 (2.868)	2.954 (2.998)	0.643 (0.818)	2 (2)
Xunfei-Xinhuo	2.639 (2.724)	3 (4)	2.391 (2.427)	2.523 (2.564)	2.745 (2.987)	2.633 (2.646)	2.904 (2.996)	0.550 (0.623)	5 (3)
Chat-Baichuan-7B	2.633 (2.821)	4 (3)	2.401 (2.651)	2.453 (2.709)	2.720 (2.998)	2.627 (2.753)	2.964 (2.993)	0.603 (0.621)	3 (4)
ChatGLM-6B	2.597 (2.644)	5 (7)	2.323 (2.312)	2.504 (2.442)	2.703 (2.956)	2.555 (2.518)	2.899 (2.989)	0.579 (0.547)	4 (5)
Chinese-LLAMA-7B	2.571 (2.723)	6 (5)	2.293 (2.431)	2.394 (2.549)	2.696 (2.993)	2.553 (2.646)	2.919 (2.998)	0.506 (0.457)	6 (7)
Ali-Tongyiqianwen	2.523 (2.646)	7 (6)	2.203 (2.309)	2.339 (2.403)	2.670 (2.983)	2.530 (2.542)	2.875 (2.991)	0.491 (0.507)	7 (6)
NewBing	2.464 (2.622)	8 (8)	2.127 (2.263)	2.144 (2.320)	2.607 (2.996)	2.550 (2.531)	2.892 (2.998)	0.415 (0.425)	9 (8)
MOSS-16B	2.337 (2.518)	9 (10)	1.994 (2.109)	2.054 (2.173)	2.498 (2.974)	2.288 (2.370)	2.849 (2.965)	0.377 (0.272)	11 (10)
Linly-ChatFlow-13B	2.312 (2.534)	11 (9)	1.966 (2.158)	2.067 (2.257)	2.408 (2.928)	2.288 (2.351)	2.830 (2.976)	0.398 (0.339)	10 (9)
ChatYuan-Large	2.312 (2.411)	11 (12)	1.933 (1.961)	2.095 (2.056)	2.458 (2.851)	2.247 (2.195)	2.826 (2.994)	0.426 (0.245)	8 (12)
MOSS-w-Plugin-16B	2.310 (2.506)	12 (11)	1.966 (2.101)	1.964 (2.150)	2.499 (2.963)	2.285 (2.352)	2.834 (2.967)	0.352 (0.254)	12 (11)

Values in parentheses are GPT-4 automated evaluation scores and rankings.

Project	Description	Paper	Code
LLMEval-Logic (arXiv 2026)	Solver-verified Chinese logical-reasoning benchmark	arXiv	GitHub
LLMEval-Fair (ACL 2026 Main)	Robust & fair evaluation across 13 disciplines, 200K+ questions	arXiv	GitHub
LLMEval-Med (EMNLP 2025 Findings)	Physician-validated clinical benchmark	arXiv	GitHub
LLMEval-2 (AAAI 2024)	Phase II: Professional domain evaluation	arXiv	GitHub

Full project list & leaderboard: llmeval.com · All datasets: 🤗 llmeval-fdu

📝 Citation

@inproceedings{zhang2024llmeval,
  title     = {LLMEval: A Preliminary Study on How to Evaluate Large Language Models},
  author    = {Yue Zhang and Ming Zhang and Haipeng Yuan and Shichun Liu and Yongyao Shi and Tao Gui and Qi Zhang and Xuanjing Huang},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume    = {38},
  number    = {17},
  pages     = {19615--19622},
  year      = {2024},
  doi       = {10.1609/aaai.v38i17.29934}
}

📞 Contact Us

This project is open to the public, and we welcome you to participate in our evaluation.

Website: https://llmeval.com/
Email: mingzhang23@m.fudan.edu.cn
WeChat: zanyingluan

LLMEval | Fudan University NLP Lab