Arena-Hard-Auto-v0.1 is an automatic evaluation tool for instruction-tuned LLMs. It contains 500 challenging user queries. We prompt GPT-4-Turbo as judge to compare the models' responses against a baseline model (default: GPT-4-0314). Although both Arena-Hard-Auto and Chatbot Arena Category Hard employ similar pipeline to select hard prompts, Arena-Hard-Auto employs automatic judge as a cheaper and faster approximator to human preference. Notably, Arena-Hard-Auto has the highest correlation and separability to Chatbot Arena among popular open-ended LLM benchmarks (see our blog post). If you are curious to see how well your model might perform on Chatbot Arena, we recommend trying Arena-Hard-Auto.
Check out our paper for more details about how Arena Hard Auto v0.1 works -> Paper link.
gpt-4-turbo-2024-04-09 | score: 82.6 | 95% CI: (-1.7, 1.9) | average #tokens: 662
gpt-4-0125-preview | score: 78.0 | 95% CI: (-2.1, 2.4) | average #tokens: 619
gemini-1.5-pro-api-preview | score: 72.0 | 95% CI: (-2.1, 2.5) | average #tokens: 676
yi-large | score: 63.7 | 95% CI: (-2.6, 2.4) | average #tokens: 626
claude-3-opus-20240229 | score: 60.4 | 95% CI: (-2.5, 2.5) | average #tokens: 541
glm-4 | score: 55.7 | 95% CI: (-2.4, 2.3) | average #tokens: 622
gpt-4-0314 | score: 50.0 | 95% CI: (0.0, 0.0) | average #tokens: 423
gemini-1.5-flash-api-preview | score: 49.6 | 95% CI: (-2.2, 2.8) | average #tokens: 642
claude-3-sonnet-20240229 | score: 46.8 | 95% CI: (-2.3, 2.7) | average #tokens: 552
claude-3-haiku-20240307 | score: 41.5 | 95% CI: (-2.5, 2.5) | average #tokens: 505
llama-3-70b-chat-hf | score: 41.1 | 95% CI: (-2.0, 2.2) | average #tokens: 583
gpt-4-0613 | score: 37.9 | 95% CI: (-2.8, 2.4) | average #tokens: 354
mistral-large-2402 | score: 37.7 | 95% CI: (-2.1, 2.6) | average #tokens: 400
mixtral-8x22b-instruct-v0.1 | score: 36.4 | 95% CI: (-2.4, 2.6) | average #tokens: 430
Qwen1.5-72B-Chat | score: 36.1 | 95% CI: (-2.0, 2.7) | average #tokens: 474
command-r-plus | score: 33.1 | 95% CI: (-2.8, 2.4) | average #tokens: 541
mistral-medium | score: 31.9 | 95% CI: (-1.9, 2.2) | average #tokens: 485
mistral-next | score: 27.4 | 95% CI: (-2.4, 2.4) | average #tokens: 297
gpt-3.5-turbo-0613 | score: 24.8 | 95% CI: (-1.9, 2.3) | average #tokens: 401
claude-2.0 | score: 24.0 | 95% CI: (-1.8, 1.8) | average #tokens: 295
dbrx-instruct | score: 23.9 | 95% CI: (-1.5, 1.5) | average #tokens: 415
Mixtral-8x7B-Instruct-v0.1 | score: 23.4 | 95% CI: (-2.0, 1.9) | average #tokens: 457
gpt-3.5-turbo-0125 | score: 23.3 | 95% CI: (-2.2, 1.9) | average #tokens: 329
Yi-34B-Chat | score: 23.1 | 95% CI: (-1.6, 1.8) | average #tokens: 611
Starling-LM-7B-beta | score: 23.0 | 95% CI: (-1.8, 1.8) | average #tokens: 530
claude-2.1 | score: 22.8 | 95% CI: (-2.3, 1.8) | average #tokens: 290
Snorkel-Mistral-PairRM-DPO | score: 20.7 | 95% CI: (-1.8, 2.2) | average #tokens: 564
llama-3-8b-chat-hf | score: 20.6 | 95% CI: (-2.0, 1.9) | average #tokens: 585
gpt-3.5-turbo-1106 | score: 18.9 | 95% CI: (-1.8, 1.6) | average #tokens: 285
gpt-3.5-turbo-0301 | score: 18.1 | 95% CI: (-1.9, 2.1) | average #tokens: 334
gemini-1.0-pro | score: 17.8 | 95% CI: (-1.2, 2.2) | average #tokens: 322
snowflake-arctic-instruct | score: 17.6 | 95% CI: (-1.8, 1.5) | average #tokens: 365
command-r | score: 17.0 | 95% CI: (-1.7, 1.8) | average #tokens: 432
phi-3-mini-128k-instruct | score: 15.4 | 95% CI: (-1.4, 1.4) | average #tokens: 609
tulu-2-dpo-70b | score: 15.0 | 95% CI: (-1.6, 1.3) | average #tokens: 550
Starling-LM-7B-alpha | score: 12.8 | 95% CI: (-1.6, 1.4) | average #tokens: 483
mistral-7b-instruct | score: 12.6 | 95% CI: (-1.7, 1.4) | average #tokens: 541
gemma-1.1-7b-it | score: 12.1 | 95% CI: (-1.3, 1.3) | average #tokens: 341
Llama-2-70b-chat-hf | score: 11.6 | 95% CI: (-1.5, 1.2) | average #tokens: 595
vicuna-33b-v1.3 | score: 8.6 | 95% CI: (-1.1, 1.1) | average #tokens: 451
gemma-7b-it | score: 7.5 | 95% CI: (-1.2, 1.3) | average #tokens: 378
Llama-2-7b-chat-hf | score: 4.6 | 95% CI: (-0.8, 0.8) | average #tokens: 561
gemma-1.1-2b-it | score: 3.4 | 95% CI: (-0.6, 0.8) | average #tokens: 316
gemma-2b-it | score: 3.0 | 95% CI: (-0.6, 0.6) | average #tokens: 369git clone https://github.com/lm-sys/arena-hard.git
cd arena-hard
pip install -r requirements.txt
pip install -r requirements-optional.txt # Optional dependencies (e.g., anthropic sdk)
We have pre-generated many popular models answers and judgments. You can browse them with an online demo or download them (with git-lfs installed) by
> git clone https://huggingface.co/spaces/lmsys/arena-hard-browser
// copy answers/judgments to the data directory
> cp -r arena-hard-browser/data . Then run
> python show_result.py
gpt-4-0125-preview | score: 78.0 | 95% CI: (-1.8, 2.2) | average #tokens: 619
claude-3-opus-20240229 | score: 60.4 | 95% CI: (-2.6, 2.1) | average #tokens: 541
gpt-4-0314 | score: 50.0 | 95% CI: (0.0, 0.0) | average #tokens: 423
claude-3-sonnet-20240229 | score: 46.8 | 95% CI: (-2.7, 2.3) | average #tokens: 552
claude-3-haiku-20240307 | score: 41.5 | 95% CI: (-2.4, 2.5) | average #tokens: 505
gpt-4-0613 | score: 37.9 | 95% CI: (-2.1, 2.2) | average #tokens: 354
mistral-large-2402 | score: 37.7 | 95% CI: (-2.9, 2.8) | average #tokens: 400
Qwen1.5-72B-Chat | score: 36.1 | 95% CI: (-2.1, 2.4) | average #tokens: 474
command-r-plus | score: 33.1 | 95% CI: (-2.0, 1.9) | average #tokens: 541Running show_results.py will save generated battles into data/arena_hard_battles.jsonl and bootstrapping statistics into data/bootstrapping_results.jsonl. If you don't want to regenerate battles or bootstrapping statistics, simply toggle argument --load-battles or --load-bootstrap, respectively.
Fill in your API endpoint in config/api_config.yaml. We support OpenAI compatible API server. You can specify parallel to indicate the number of concurrent API requests (default: 1).
# example
gpt-3.5-turbo-0125:
model_name: gpt-3.5-turbo-0125
endpoints: null
api_type: openai
parallel: 8
[YOUR-MODEL-NAME]:
model_name: [YOUR-MODEL-NAME]
endpoints:
- api_base: [YOUR-ENDPOINT-URL]
api_key: [YOUR-API-KEY]
api_type: openai
parallel: 8You may use inference engine such as vLLM or SGLang to host your model with an OpenAI compatible API server.
In config/gen_answer_config.yaml, add your model name in model_list.
bench_name: arena-hard-v0.1
temperature: 0.0
max_tokens: 4096
num_choices: 1
model_list:
- [YOUR-MODEL-NAME]Run the command to generate answers:
python gen_answer.pyCaching feature is implemented. The code will skip generating an answer when there is already an existing answer/judgment to the same prompt.
In config/judge_config.yaml, add your model name in model_list.
...
# Add your model below for evaluation
model_list:
- gpt-3.5-turbo-0125
- [YOUR-MODEL-NAME]Run the command to generate judgments:
python gen_judgment.pyJudgment caching is also implemented. It will skip generating judgments that has already been generated or lacks one of the model answers.
Output model win rates. Optionally, use --full-stats for detailed results.
> python show_result.pyYou can review individual judgment results using our UI code.
> python qa_broswer.py --shareComing soon...
The code in this repository is mostly developed for or derived from the paper below. Please cite it if you find the repository helpful.
@misc{li2024crowdsourced,
title={From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline},
author={Tianle Li and Wei-Lin Chiang and Evan Frick and Lisa Dunlap and Tianhao Wu and Banghua Zhu and Joseph E. Gonzalez and Ion Stoica},
year={2024},
eprint={2406.11939},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
