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

• URL: http://arxiv.org/abs/2408.13533v1
• Date: Sat, 24 Aug 2024 09:23:01 GMT
• Title: Pandora's Box or Aladdin's Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language Models
• Authors: Jinyang Wu, Feihu Che, Chuyuan Zhang, Jianhua Tao, Shuai Zhang, Pengpeng Shao,
• Abstract summary: Retrieval-Augmented Generation (RAG) has emerged as a crucial method for addressing hallucinations in large language models (LLMs) In this paper, we define seven distinct noise types from a linguistic perspective and establish a Noise RAG Benchmark (NoiserBench) Our analysis offers insights for developing more robust, adaptable RAG solutions and mitigating hallucinations across diverse retrieval scenarios.
• Score: 25.850830204451363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training [39.21885486667879]
  Large Language Models (LLMs) exhibit substantial capabilities yet encounter challenges, including hallucination, outdated knowledge, and untraceable reasoning processes. Retrieval-augmented generation (RAG) has emerged as a promising solution, integrating knowledge from external databases to mitigate these challenges. We propose a novel RAG approach known as Retrieval-augmented Adaptive Adrial Training (RAAT)
  arXiv  Detail & Related papers  (2024-05-31T16:24:53Z)
• NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity Recognition [3.726602636064681]
  We present an analysis that shows that real noise is significantly more challenging than simulated noise. We show that current state-of-the-art models for noise-robust learning fall far short of their theoretically achievable upper bound.
  arXiv  Detail & Related papers  (2024-05-13T10:20:31Z)
• ROPO: Robust Preference Optimization for Large Language Models [59.10763211091664]
  We propose an iterative alignment approach that integrates noise-tolerance and filtering of noisy samples without the aid of external models. Experiments on three widely-used datasets with Mistral-7B and Llama-2-7B demonstrate that ROPO significantly outperforms existing preference alignment methods.
  arXiv  Detail & Related papers  (2024-04-05T13:58:51Z)
• Large Language Models are Efficient Learners of Noise-Robust Speech Recognition [65.95847272465124]
  Recent advances in large language models (LLMs) have promoted generative error correction (GER) for automatic speech recognition (ASR) In this work, we extend the benchmark to noisy conditions and investigate if we can teach LLMs to perform denoising for GER. Experiments on various latest LLMs demonstrate our approach achieves a new breakthrough with up to 53.9% correction improvement in terms of word error rate.
  arXiv  Detail & Related papers  (2024-01-19T01:29:27Z)
• RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models [9.465753274663061]
  Retrieval-augmented generation (RAG) has become a main technique for alleviating hallucinations in large language models (LLMs) This paper presents RAGTruth, a corpus tailored for analyzing word-level hallucinations in various domains.
  arXiv  Detail & Related papers  (2023-12-31T04:43:45Z)
• Benchmarking Large Language Models in Retrieval-Augmented Generation [53.504471079548]
  We systematically investigate the impact of Retrieval-Augmented Generation on large language models. We analyze the performance of different large language models in 4 fundamental abilities required for RAG. We establish Retrieval-Augmented Generation Benchmark (RGB), a new corpus for RAG evaluation in both English and Chinese.
  arXiv  Detail & Related papers  (2023-09-04T08:28:44Z)
• Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models [116.01843550398183]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks. LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.
  arXiv  Detail & Related papers  (2023-09-03T16:56:48Z)
• An Investigation of Noise in Morphological Inflection [21.411766936034]
  We investigate the types of noise encountered within a pipeline for truly unsupervised morphological paradigm completion. We compare the effect of different types of noise on multiple state-of-the-art inflection models. We propose a novel character-level masked language modeling (CMLM) pretraining objective and explore its impact on the models' resistance to noise.
  arXiv  Detail & Related papers  (2023-05-26T02:14:34Z)
• Adaptive Multi-View ICA: Estimation of noise levels for optimal inference [65.94843987207445]
  Adaptive multiView ICA (AVICA) is a noisy ICA model where each view is a linear mixture of shared independent sources with additive noise on the sources. On synthetic data, AVICA yields better sources estimates than other group ICA methods thanks to its explicit MMSE estimator. On real magnetoencephalograpy (MEG) data, we provide evidence that the decomposition is less sensitive to sampling noise and that the noise variance estimates are biologically plausible.
  arXiv  Detail & Related papers  (2021-02-22T13:10:12Z)
• Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning [70.57193072829288]
  Low-rank Multi-view Subspace Learning has shown great potential in cross-view classification in recent years. Existing LMvSL based methods are incapable of well handling view discrepancy and discriminancy simultaneously. We propose Structured Low-rank Matrix Recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy.
  arXiv  Detail & Related papers  (2020-03-22T03:57:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.