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https://github.com/YJiangcm/FollowBench
Code for "FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models (ACL 2024)"
https://github.com/YJiangcm/FollowBench
benchmark constraints instruction-following large-language-models multi-level
Last synced: 2 months ago
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Code for "FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models (ACL 2024)"
- Host: GitHub
- URL: https://github.com/YJiangcm/FollowBench
- Owner: YJiangcm
- License: apache-2.0
- Created: 2023-10-31T01:21:40.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2024-08-19T02:40:27.000Z (4 months ago)
- Last Synced: 2024-08-19T03:45:11.651Z (4 months ago)
- Topics: benchmark, constraints, instruction-following, large-language-models, multi-level
- Language: Python
- Homepage: https://arxiv.org/abs/2310.20410
- Size: 3.91 MB
- Stars: 67
- Watchers: 3
- Forks: 10
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-llm-if - FollowBench
- awesome-llm-if - FollowBench
README
[](https://github.com/YJiangcm/FollowBench)
[](https://huggingface.co/datasets/YuxinJiang/FollowBench)
# FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models (ACL 2024)
We introduce **FollowBench**, a Multi-level Fine-grained Constraints Following Benchmark for **systemically** and **precisely** evaluate the instruction-following capability of LLMs.
- **FollowBench** comprehensively includes five different types (i.e., Content, Situation, Style, Format, and Example) of _fine-grained constraints_.
- To enable a precise constraint following estimation on diverse difficulties, we introduce a _Multi-level_ mechanism that incrementally adds a single constraint to the initial instruction at each increased level.
- To evaluate whether LLMs' outputs have satisfied every individual constraint, we propose to prompt strong LLMs with _constraint-evolution paths_ to handle challenging open-ended instructions.
- By evaluating **13** closed-source and open-source popular LLMs on FollowBench, we highlight the weaknesses of LLMs in instruction following and point towards potential avenues for future work.
## 🔥 Updates
* 2024/05/16: We are delighted that FollowBench has been accepted to ACL 2024 main conference!
* 2024/01/11: We have uploaded the English and Chinese version of FollowBench to [Hugging Face](https://huggingface.co/datasets/YuxinJiang/FollowBench).
* 2023/12/20: We evaluated Qwen-Chat-72B/14B/7B on FollowBench, check it in [Leaderboard](#leaderboard).
* 2023/12/15: We released a Chinese version of FolllowBench, check it in [data_zh/](data_zh/).
* 2023/11/14: We released the second verson of our [paper](https://arxiv.org/abs/2310.20410). Check it out!
* 2022/11/10: We released the data and code of FollowBench.
* 2023/10/31: We released the first verson of our [paper](https://arxiv.org/abs/2310.20410v1). Check it out!
## 🔍 Table of Contents
- [🖥️ Leaderboard](#leaderboard)
- [📄 Data of FollowBench](#data-of-followbench)
- [⚙️ How to Evaluate on FollowBench](#how-to-evaluate-on-followbench)
- [📝 Citation](#citation)
### Metrics
* **Hard Satisfaction Rate (HSR):** the average rate at which all constraints of individual instructions are fully satisfied
* **Soft Satisfaction Rate (SSR):** the average satisfaction rate of individual constraints across all instructions
* **Consistent Satisfaction Levels (CSL):** how many consecutive levels a model can satisfy, beginning from level 1
### Level-categorized Results
#### English
#### Chinese
### Constraint-categorized Results
#### English
#### Chinese
## 📄 Data of FollowBench
The data of FollowBench can be found in [data/](data/).
We also provide a **Chinese version** of FollowBench in [data_zh/](data_zh/).
## ⚙️ How to Evaluate on FollowBench
#### Install Dependencies
```
conda create -n followbench python=3.10
conda activate followbench
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -r requirements.txt
```
#### Model Inference
```bash
cd FollowBench/
python code/model_inference.py --model_path
```
#### LLM-based Evaluation
```bash
cd FollowBench/
python code/llm_eval.py --model_path --api_key
```
#### Merge Evaluation and Save Results
Next, we conduct **rule-based evaluation** and merge the **rule-based evaluation** results and **LLM-based evaluation** results using the following script:
```bash
cd FollowBench/
python code/eval.py --model_paths
```
The final results will be saved in the folder named ```evaluation_result```.
## 📝 Citation
Please cite our paper if you use the data or code in this repo.
```
@inproceedings{jiang-etal-2024-followbench,
title = "{F}ollow{B}ench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models",
author = "Jiang, Yuxin and
Wang, Yufei and
Zeng, Xingshan and
Zhong, Wanjun and
Li, Liangyou and
Mi, Fei and
Shang, Lifeng and
Jiang, Xin and
Liu, Qun and
Wang, Wei",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.257",
pages = "4667--4688",
}
```