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BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities

This repository contains the evaluation code, prompt, and datasets for the paper BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities.

BAMBOO benchmark is a comprehensive benchmark to analyze LLMs’ long text modeling. In BAMBOO benchmark, there are 10 datasets from 5 tasks, i.e., question answering, hallucination detection, langauge modeling, code completion, and text sorting. Our benchmark is constructed with the following principles:

Comprehensive Capacity Evaluation
Avoidance of Data Contamination
Accurate Automatic Evaluation
Different Length Levels

Repository Structure

datasets: This directory contains the data files in the benchmark. There are 10 datasets, and each dataset contains 2 files of different lengths(4k, 16k).
evaluate.py: This Python script is used to evaluate the outputs of your long text models.
prompt.json: This json file contains prompts used to evaluating your long context model.
requirements.txt: Python packages to be installed for evaluating your outputs.
private_eval: The directory contains evaluate code of private_eval datasets, which refers to PyCodeGPT

Evaluation

If you obtain outputs of each datasets, you can create a python environment BAMBOO and evaluate your outputs.

Create and activate the conda environment named BAMBOO:
```
conda create -n BAMBOO
conda activate BAMBOO
```
Install the required Python packages by running:
```
pip install -r requirements.txt
```

Run the generation script with your model. For example:

python code/run_for_all.py --output_path output_path --input_path input_path --prompt_name prompt_name --prompt_path prompt_path --model_path model_path

Run the evaluation script with your model's output. For example:
```
python evaluate.py --input_path your_file.jsonl --task task
```

Evaluating a model from huggingface

Here, we provide instructions on utilizing a model from Hugging Face to generate responses to prompts in BAMBOO. We employ the model lmsys/vicuna-7b-v1.5-16k, along with its template in fastchat, as an illustrative example. You can execute the following script to evaluate altqa_4K with the aforementioned model:

python run_for_all.py --output_path pred_altqa_long_longchat.jsonl --input_path datasets/altqa_middle.jsonl --prompt_name altqa --prompt_path prompt.json --model_path lmsys/vicuna-7b-v1.5-16k

Output format

Each data point in your jsonl file should at least contains two keys:

pred: prediction of the model.
answer: the right answer.

Task Selection

Task should chosen from the list ['meetingqa','paperqa','altqa','senhallu','abshallu','meetingpred','showspred','reportsumsort','showssort','private_eval']

License

This repository is released under the MIT License.

Citation

If you use this benchmark or code in your research, please consider citing the original paper:

@article{dong2023bamboo,
  title={BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models},
  author={Dong, Zican and Tang, Tianyi and Li, Junyi and Zhao, Wayne Xin and Wen, Ji-Rong},
  journal={arXiv preprint arXiv:2309.13345},
  year={2023}
}