| --- |
| license: mit |
| task_categories: |
| - question-answering |
| - text-generation |
| language: |
| - en |
| size_categories: |
| - 1K<n<10K |
| tags: |
| - rag |
| - noise |
| - benchmark |
| - retrieval-augmented-generation |
| - llm-evaluation |
| --- |
| |
| # Dataset Card for NoiserBench |
|
|
| This dataset card describes NoiserBench, a comprehensive evaluation framework for analyzing the role of noise in Retrieval-Augmented Generation (RAG) systems with Large Language Models. |
|
|
| ## Dataset Details |
|
|
| ### Dataset Description |
|
|
| NoiserBench is a comprehensive benchmark designed to evaluate how different types of noise affect Large Language Models in Retrieval-Augmented Generation scenarios. The benchmark encompasses multiple datasets and reasoning tasks, specifically designed to analyze seven distinct noise types from a linguistic perspective. This framework reveals that noise can be categorized into two practical groups: beneficial noise (which may enhance model capabilities) and harmful noise (which generally impairs performance). |
|
|
| - **Language(s) (NLP):** English |
| - **License:** MIT |
| - **Paper:** [Pandora's Box or Aladdin's Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language Models](https://arxiv.org/abs/2408.13533) |
|
|
| ### Dataset Sources |
|
|
| - **Repository:** https://github.com/jinyangwu/NoiserBench |
| - **Paper:** https://arxiv.org/abs/2408.13533 |
|
|
| ## Uses |
|
|
| NoiserBench is designed for: |
| - Evaluating the robustness of RAG systems under different noise conditions |
| - Analyzing how various noise types affect LLM performance in retrieval scenarios |
| - Benchmarking different LLM architectures and scales on noisy retrieval tasks |
| - Research into developing more robust and adaptable RAG solutions |
| - Understanding the distinction between beneficial and harmful noise in RAG contexts |
|
|
| ## Dataset Structure |
|
|
| The benchmark encompasses multiple datasets and reasoning tasks designed to evaluate seven distinct noise types from a linguistic perspective. The framework categorizes noise into: |
|
|
| 1. **Beneficial Noise**: Types of noise that may enhance model capabilities and overall performance |
| 2. **Harmful Noise**: Types of noise that generally impair LLM performance |
|
|
| The evaluation framework includes various reasoning tasks to comprehensively assess how different LLM architectures respond to these noise categories. |
|
|
| ## Citation |
|
|
| **BibTeX:** |
|
|
| ```bibtex |
| @article{wu2024pandora, |
| title={Pandora's Box or Aladdin's Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language Models}, |
| author={Wu, Jinyang and Che, Feihu and Zhang, Chuyuan and Tao, Jianhua and Zhang, Shuai and Shao, Pengpeng}, |
| journal={arXiv preprint arXiv:2408.13533}, |
| year={2024} |
| } |
| ``` |
|
|
| **APA:** |
|
|
| Wu, J., Che, F., Zhang, C., Tao, J., Zhang, S., & Shao, P. (2024). Pandora's Box or Aladdin's Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language Models. arXiv preprint arXiv:2408.13533. |
|
|
| ## Glossary |
|
|
| - **RAG (Retrieval-Augmented Generation)**: A method that combines information retrieval with text generation to reduce hallucinations in large language models |
| - **Beneficial Noise**: Types of noise that may enhance certain aspects of model capabilities and overall performance |
| - **Harmful Noise**: Types of noise that generally impair LLM performance in RAG scenarios |
| - **NoiserBench**: The comprehensive evaluation framework established in this work |
|
|
| ## Dataset Card Contact |
|
|
| For questions about this dataset card or the underlying benchmark, please refer to the code repository or contact me at wu-jy23@mails.tsinghua.edu.cn. |
