Tackling the Inherent Difficulty of Noise Filtering in RAG

2026-01-05

arXiv.org

AI Summary

This work addresses the vulnerability of retrieval-augmented generation (RAG) systems to performance degradation and hallucination caused by irrelevant or noisy retrieved documents. To overcome the limitations of existing approaches in effectively filtering such noise, the paper proposes a novel fine-tuning strategy that transcends conventional constraints on attention architecture. Specifically, the method introduces a tailored training objective and targeted modifications to the attention mechanism to enhance the model’s ability to discriminate between relevant and irrelevant retrieved content. This approach substantially improves the model’s capacity for information filtering and robustness in noisy retrieval settings. Experimental results demonstrate that the proposed method significantly outperforms standard fine-tuning and alternative noise-filtering techniques across multiple benchmark datasets.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Calibration & Uncertainty Quantification
• 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
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Retrieval-Augmented Generation (RAG) has become a widely adopted approach to enhance Large Language Models (LLMs) by incorporating external knowledge and reducing hallucinations. However, noisy or irrelevant documents are often introduced during RAG, potentially degrading performance and even causing hallucinated outputs. While various methods have been proposed to filter out such noise, we argue that identifying irrelevant information from retrieved content is inherently difficult and limited number of transformer layers can hardly solve this. Consequently, retrievers fail to filter out irrelevant documents entirely. Therefore, LLMs must be robust against such noise, but we demonstrate that standard fine-tuning approaches are often ineffective in enabling the model to selectively utilize relevant information while ignoring irrelevant content due to the structural constraints of attention patterns. To address this, we propose a novel fine-tuning method designed to enhance the model's ability to distinguish between relevant and irrelevant information within retrieved documents. Extensive experiments across multiple benchmarks show that our approach significantly improves the robustness and performance of LLMs.

Problem

• Retrieval-Augmented Generation
• noise filtering
• irrelevant information
• hallucination
• large language models

Innovation

• Retrieval-Augmented Generation
• noise filtering
• fine-tuning
• attention mechanism
• robustness