| --- |
| license: apache-2.0 |
| dataset_info: |
| features: |
| - name: question_id |
| dtype: string |
| - name: category |
| dtype: string |
| - name: cluster |
| dtype: string |
| - name: turns |
| list: |
| - name: content |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 251691 |
| num_examples: 500 |
| download_size: 154022 |
| dataset_size: 251691 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| --- |
| |
| ## Arena-Hard-Auto |
|
|
| **Arena-Hard-Auto-v0.1** ([See Paper](https://arxiv.org/abs/2406.11939)) is an automatic evaluation tool for instruction-tuned LLMs. It contains 500 challenging user queries sourced from Chatbot Arena. We prompt GPT-4-Turbo as judge to compare the models' responses against a baseline model (default: GPT-4-0314). Notably, Arena-Hard-Auto has the highest *correlation* and *separability* to Chatbot Arena among popular open-ended LLM benchmarks ([See Paper](https://arxiv.org/abs/2406.11939)). If you are curious to see how well your model might perform on Chatbot Arena, we recommend trying Arena-Hard-Auto. |
|
|
| Please checkout our GitHub repo on how to evaluate models using Arena-Hard-Auto and more information about the benchmark. |
|
|
| If you find this dataset useful, feel free to cite us! |
| ``` |
| @article{li2024crowdsourced, |
| title={From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline}, |
| author={Li, Tianle and Chiang, Wei-Lin and Frick, Evan and Dunlap, Lisa and Wu, Tianhao and Zhu, Banghua and Gonzalez, Joseph E and Stoica, Ion}, |
| journal={arXiv preprint arXiv:2406.11939}, |
| year={2024} |
| } |
| ``` |
|
|
