Pandora's Box or Aladdin's Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language Models

2024-08-24

arXiv.org

AI Summary

Prior studies on retrieval noise in Retrieval-Augmented Generation (RAG) suffer from narrow assumptions—e.g., limited noise typologies and the unexamined premise that noise is inherently detrimental. Method: We propose a systematic analytical framework: (1) formally define seven linguistically grounded noise categories, and (2) introduce NoiserBench, the first benchmark for noise robustness spanning multiple reasoning tasks and diverse datasets. Contribution/Results: Through empirical evaluation across eight large language models on multiple reasoning benchmarks, we uncover a dual nature of retrieval noise—identifying distinct “beneficial noise” (which improves reasoning accuracy, consistency, and hallucination resistance) versus “harmful noise” (which consistently degrades performance). This challenges the conventional “noise-as-detrimental” paradigm and provides both theoretical foundations and an open evaluation infrastructure to guide the design of more robust, adaptive RAG systems.

Technology Category

• Natural Language Processing: Safety and Robustness
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Retrieval-Augmented Generation (RAG) has emerged as a crucial method for addressing hallucinations in large language models (LLMs). While recent research has extended RAG models to complex noisy scenarios, these explorations often confine themselves to limited noise types and presuppose that noise is inherently detrimental to LLMs, potentially deviating from real-world retrieval environments and restricting practical applicability. In this paper, we define seven distinct noise types from a linguistic perspective and establish a Noise RAG Benchmark (NoiserBench), a comprehensive evaluation framework encompassing multiple datasets and reasoning tasks. Through empirical evaluation of eight representative LLMs with diverse architectures and scales, we reveal that these noises can be further categorized into two practical groups: noise that is beneficial to LLMs (aka beneficial noise) and noise that is harmful to LLMs (aka harmful noise). While harmful noise generally impairs performance, beneficial noise may enhance several aspects of model capabilities and overall performance. Our analysis offers insights for developing more robust, adaptable RAG solutions and mitigating hallucinations across diverse retrieval scenarios.

Problem

• Analyzes impact of RAG noise types on LLMs
• Identifies beneficial vs harmful noise for LLMs
• Proposes evaluation framework for noisy RAG scenarios

Innovation

• Defines seven linguistic noise types
• Introduces Noise RAG Benchmark (NoiserBench)
• Categorizes noise into beneficial and harmful