Optimizing Knowledge Integration in Retrieval-Augmented Generation with Self-Selection

• URL: http://arxiv.org/abs/2502.06148v1
• Date: Mon, 10 Feb 2025 04:29:36 GMT
• Title: Optimizing Knowledge Integration in Retrieval-Augmented Generation with Self-Selection
• Authors: Yan Weng, Fengbin Zhu, Tong Ye, Haoyan Liu, Fuli Feng, Tat-Seng Chua,
• Abstract summary: Retrieval-Augmented Generation (RAG) has proven effective in enabling Large Language Models (LLMs) to produce more accurate and reliable responses. We propose a novel Self-Selection RAG framework, where the LLM is made to select from pairwise responses generated with internal parametric knowledge solely.
• Score: 72.92366526004464
• License:
• Abstract: Retrieval-Augmented Generation (RAG), which integrates external knowledge into Large Language Models (LLMs), has proven effective in enabling LLMs to produce more accurate and reliable responses. However, it remains a significant challenge how to effectively integrate external retrieved knowledge with internal parametric knowledge in LLMs. In this work, we propose a novel Self-Selection RAG framework, where the LLM is made to select from pairwise responses generated with internal parametric knowledge solely and with external retrieved knowledge together to achieve enhanced accuracy. To this end, we devise a Self-Selection-RGP method to enhance the capabilities of the LLM in both generating and selecting the correct answer, by training the LLM with Direct Preference Optimization (DPO) over a curated Retrieval Generation Preference (RGP) dataset. Experimental results with two open-source LLMs (i.e., Llama2-13B-Chat and Mistral-7B) well demonstrate the superiority of our approach over other baseline methods on Natural Questions (NQ) and TrivialQA datasets.

Related papers

• LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization [59.75242204923353]
  We introduce LLM-Lasso, a framework that leverages large language models (LLMs) to guide feature selection in Lasso regression. LLMs generate penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model. Features identified as more relevant by the LLM receive lower penalties, increasing their likelihood of being retained in the final model.
  arXiv  Detail & Related papers  (2025-02-15T02:55:22Z)
• Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering [66.5524727179286]
  NOVA is a framework designed to identify high-quality data that aligns well with the learned knowledge to reduce hallucinations. It includes Internal Consistency Probing (ICP) and Semantic Equivalence Identification (SEI) to measure how familiar the LLM is with instruction data. To ensure the quality of selected samples, we introduce an expert-aligned reward model, considering characteristics beyond just familiarity.
  arXiv  Detail & Related papers  (2025-02-11T08:05:56Z)
• LLM Alignment as Retriever Optimization: An Information Retrieval Perspective [44.26715637344781]
  Large Language Models (LLMs) have revolutionized artificial intelligence with capabilities in reasoning, coding, and communication. Our work introduces a novel direct optimization approach for LLM alignment by drawing on established Information Retrieval (IR) principles. Building on this foundation, we propose LLM Alignment as Retriever Preference Optimization (LarPO), a new alignment method that enhances overall alignment quality.
  arXiv  Detail & Related papers  (2025-02-06T01:22:06Z)
• Parametric Retrieval Augmented Generation [32.29608109539912]
  Parametric RAG is a new RAG paradigm that integrates external knowledge directly into the parameters of feed-forward networks. It substantially enhances both the effectiveness and efficiency of knowledge augmentation in large language models.
  arXiv  Detail & Related papers  (2025-01-27T10:04:49Z)
• Invar-RAG: Invariant LLM-aligned Retrieval for Better Generation [43.630437906898635]
  We propose a novel two-stage fine-tuning architecture called Invar-RAG. In the retrieval stage, an LLM-based retriever is constructed by integrating LoRA-based representation learning. In the generation stage, a refined fine-tuning method is employed to improve LLM accuracy in generating answers based on retrieved information.
  arXiv  Detail & Related papers  (2024-11-11T14:25:37Z)
• Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output [49.893971654861424]
  We present a light-weight approach for detecting nonfactual outputs from retrieval-augmented generation (RAG) We compute a factuality score that can be thresholded to yield a binary decision. Our experiments show high area under the ROC curve (AUC) across a wide range of relevant open source datasets.
  arXiv  Detail & Related papers  (2024-11-01T20:44:59Z)
• A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential [20.1396255995056]
  Retrieval-Augmented Generation (RAG) is an effective solution to supplement necessary knowledge to large language models (LLMs) "generate-then-read" pipeline is proposed to replace the retrieval stage with generation from the LLM itself. This paper formalizes a general "A + B" framework with varying combinations of foundation models and types for systematic investigation.
  arXiv  Detail & Related papers  (2024-06-06T11:14:27Z)
• One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models [67.49462724595445]
  Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs) We propose a novel method that involves learning scalable and pluggable virtual tokens for RAG.
  arXiv  Detail & Related papers  (2024-05-30T03:44:54Z)
• CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control [26.21425058462886]
  Retrieval-augmented generation (RAG) has emerged as a promising solution for mitigating hallucinations of large language models (LLMs) with retrieved external knowledge. We present the first attempts to solve adaptive RAG from a representation perspective and develop an inherent control-based framework, termed name. Experiments show that name is superior to existing adaptive RAG methods on a diverse set of tasks.
  arXiv  Detail & Related papers  (2024-05-29T03:17:16Z)
• ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented Agents [49.30553350788524]
  Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to leverage external knowledge. Existing RAG models often treat LLMs as passive recipients of information. We introduce ActiveRAG, a multi-agent framework that mimics human learning behavior.
  arXiv  Detail & Related papers  (2024-02-21T06:04:53Z)

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.