OpenCompass/docs/en/advanced_guides/needleinahaystack_eval.md

Needle In A Haystack Experimental Evaluation

Introduction to the Needle In A Haystack Test

The Needle In A Haystack test (inspired by NeedleInAHaystack) is an evaluation method that randomly inserts key information into long texts to form prompts for large language models (LLMs). The test aims to detect whether large models can extract such key information from extensive texts, thereby assessing the models' capabilities in processing and understanding long documents.

Task Overview

Within the NeedleBench framework of OpenCompass, we have designed a series of increasingly challenging test scenarios to comprehensively evaluate the models' abilities in long text information extraction and reasoning:

• Single-Needle Retrieval Task (S-RT): Assesses an LLM's ability to extract a single key piece of information from a long text, testing its precision in recalling specific details within broad narratives. This corresponds to the original Needle In A Haystack test setup.

• Multi-Needle Retrieval Task (M-RT): Explores an LLM's capability to retrieve multiple related pieces of information from long texts, simulating real-world scenarios of complex queries on comprehensive documents.

• Multi-Needle Reasoning Task (M-RS): Evaluates an LLM's long-text abilities by extracting and utilizing multiple key pieces of information, requiring the model to have a comprehensive understanding of each key information fragment.

• Ancestral Trace Challenge (ATC): Uses the "relational needle" to test an LLM's ability to handle multi-layer logical challenges in real long texts. In the ATC task, a series of logical reasoning questions are used to test the model's memory and analytical skills for every detail in the text. For this task, we remove the irrelevant text (Haystack) setting, designing all texts as critical information, requiring the LLM to use all the content and reasoning in the text accurately to answer the questions.

Evaluation Steps

1. Download the dataset from here.

2. Place the downloaded files in the opencompass/data/needlebench/ directory. The expected file structure in the needlebench directory is shown below:

opencompass/
├── configs
├── docs
├── data
│   └── needlebench
│       ├── multi_needle_reasoning_en.json
│       ├── multi_needle_reasoning_zh.json
│       ├── names.json
│       ├── needles.jsonl
│       ├── PaulGrahamEssays.jsonl
│       ├── zh_finance.jsonl
│       ├── zh_game.jsonl
│       ├── zh_government.jsonl
│       ├── zh_movie.jsonl
│       ├── zh_tech.jsonl
│       ├── zh_general.jsonl
├── LICENSE
├── opencompass
├── outputs
├── run.py
├── more...

OpenCompass Environment Setup

conda create --name opencompass python=3.10 pytorch torchvision pytorch-cuda -c nvidia -c pytorch -y
conda activate opencompass
git clone https://github.com/open-compass/opencompass opencompass
cd opencompass
pip install -e .

Configuring the Dataset

We have pre-configured datasets for common text lengths (4k, 8k, 32k, 128k, 200k, 1000k) in configs/datasets/needlebench, allowing you to flexibly create datasets that meet your needs by defining related parameters in the configuration files.

Example of Evaluation

Evaluating using the InternLM2-7B model deployed with LMDeploy

For instance, to evaluate all tasks in NeedleBench-4K using the InternLM2-7B model deployed with LMDeploy, use the following command line command that calls the predefined model and dataset configuration files, without needing to write additional configuration files:

python run.py --dataset needlebench_4k --models lmdeploy_internlm2_chat_7b --summarizer needlebench/needlebench_4k_summarizer --slurm -p partition_name -q reserved --max-num-workers 32 --max-partition-size 8000

If you only want to test the original Needle In A Haystack task setup, you can change the dataset parameter to needlebench_single_4k, such as:

python run.py --dataset needlebench_single_4k --models lmdeploy_internlm2_chat_7b --summarizer needlebench/needlebench_4k_summarizer --sl

urm -p partition_name -q reserved --max-num-workers 32 --max-partition-size 8000

You can also choose sub-datasets, such as changing the --datasets parameter to needlebench_single_4k/needlebench_zh_datasets for only testing the Chinese version of the single needle task, where the parameter after / represents the sub-dataset. You can find the optional sub-dataset variables in the configs/datasets/needlebench/needlebench_4k/needlebench_single_4k.py, such as:

python run.py --dataset needlebench_single_4k/needlebench_zh_datasets --models lmdeploy_internlm2_chat_7b --summarizer needlebench/needlebench_4k_summarizer --slurm -p partition_name -q reserved --max-num-workers 32 --max-partition-size 8000

Be sure to install the LMDeploy tool before starting the evaluation:

pip install lmdeploy

This command initiates the evaluation process, with parameters -p partition_name -q auto and --max-num-workers 32 used to specify the Slurm partition name and the maximum number of worker processes.

Evaluating Other Huggingface Models

For other models, we recommend writing an additional configuration file to modify the model's max_seq_len and max_out_len parameters so the model can receive the complete long text content, as we have prepared in the configs/eval_needlebench.py file. The complete content is as follows:

from mmengine.config import read_base
with read_base():
    from .models.hf_internlm.lmdeploy_internlm2_chat_7b import models as internlm2_chat_7b_200k
    from .models.hf_internlm.hf_internlm2_chat_7b import models as internlm2_chat_7b

    # Evaluate needlebench_4k, adjust the configuration to use 8k, 32k, 128k, 200k, or 1000k if necessary.
    # from .datasets.needlebench.needlebench_4k.needlebench_4k import needlebench_datasets
    # from .summarizers.needlebench import needlebench_4k_summarizer as summarizer

    # only eval original "needle in a haystack test" in needlebench_4k
    from .datasets.needlebench.needlebench_4k.needlebench_single_4k import needlebench_zh_datasets, needlebench_en_datasets
    from .summarizers.needlebench import needlebench_4k_summarizer as summarizer

    # eval Ancestral Tracing Challenge(ATC)
    # from .datasets.needlebench.atc.atc_choice_50 import needlebench_datasets
    # from .summarizers.needlebench import atc_summarizer_50 as summarizer

datasets = sum([v for k, v in locals().items() if ('datasets' in k)], [])

for m in internlm2_chat_7b:
    m['max_seq_len'] = 32768 # Ensure InternLM2-7B model can receive the complete long text, other models need to adjust according to their maximum sequence length support.
    m['max_out_len'] = 2000 # Ensure that in the multi-needle recall task, the model can receive a complete response

models = internlm2_chat_7b

work_dir = './outputs/needlebench'

Once the test config file is written, we can pass the corresponding config file path through the run.py file in the command line, such as:

python run.py configs/eval_needlebench.py --slurm -p partition_name -q reserved --max-num-workers 128 --max-partition-size 8000

Note, at this point, we do not need to pass in the --dataset, --models, --summarizer parameters, as we have already defined these configurations in the config file. You can manually adjust the --max-partition-size setting to achieve the best task slicing strategy to improve evaluation efficiency.

Visualization

We have built-in result visualization into the summarizer implementation in the latest code version. You can find the corresponding visualizations in the plots directory of the respective output folder, eliminating the need for manual visualization of scores across various depths and lengths.

If you use this method, please add a reference:

@misc{2023opencompass,
    title={OpenCompass: A Universal Evaluation Platform for Foundation Models},
    author={OpenCompass Contributors},
    howpublished={\url{https://github.com/open-compass/opencompass}},
    year={2023}


}

@misc{LLMTest_NeedleInAHaystack,
  title={LLMTest Needle In A Haystack - Pressure Testing LLMs},
  author={gkamradt},
  year={2023},
  howpublished={\url{https://github.com/gkamradt/LLMTest_NeedleInAHaystack}}
}

@misc{wei2023skywork,
      title={Skywork: A More Open Bilingual Foundation Model},
      author={Tianwen Wei and Liang Zhao and Lichang Zhang and Bo Zhu and Lijie Wang and Haihua Yang and Biye Li and Cheng Cheng and Weiwei Lü and Rui Hu and Chenxia Li and Liu Yang and Xilin Luo and Xuejie Wu and Lunan Liu and Wenjun Cheng and Peng Cheng and Jianhao Zhang and Xiaoyu Zhang and Lei Lin and Xiaokun Wang and Yutuan Ma and Chuanhai Dong and Yanqi Sun and Yifu Chen and Yongyi Peng and Xiaojuan Liang and Shuicheng Yan and Han Fang and Yahui Zhou},
      year={2023},
      eprint={2310.19341},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}