Update README.md

README.md

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----
-license: mit
----
+---
+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 [email protected].