Systematic Knowledge Injection into Large Language Models via Diverse Augmentation for Domain-Specific RAG

• URL: http://arxiv.org/abs/2502.08356v2
• Date: Mon, 17 Feb 2025 14:29:48 GMT
• Title: Systematic Knowledge Injection into Large Language Models via Diverse Augmentation for Domain-Specific RAG
• Authors: Kushagra Bhushan, Yatin Nandwani, Dinesh Khandelwal, Sonam Gupta, Gaurav Pandey, Dinesh Raghu, Sachindra Joshi,
• Abstract summary: Retrieval-Augmented Generation (RAG) has emerged as a prominent method for incorporating domain knowledge into Large Language Models (LLMs) We present a novel framework that significantly enhances the fine-tuning process by augmenting the training data in two ways -- context augmentation and knowledge paraphrasing.
• Score: 24.660769275714685
• License:
• Abstract: Retrieval-Augmented Generation (RAG) has emerged as a prominent method for incorporating domain knowledge into Large Language Models (LLMs). While RAG enhances response relevance by incorporating retrieved domain knowledge in the context, retrieval errors can still lead to hallucinations and incorrect answers. To recover from retriever failures, domain knowledge is injected by fine-tuning the model to generate the correct response, even in the case of retrieval errors. However, we observe that without systematic knowledge augmentation, fine-tuned LLMs may memorize new information but still fail to extract relevant domain knowledge, leading to poor performance. In this work, we present a novel framework that significantly enhances the fine-tuning process by augmenting the training data in two ways -- context augmentation and knowledge paraphrasing. In context augmentation, we create multiple training samples for a given QA pair by varying the relevance of the retrieved information, teaching the model when to ignore and when to rely on retrieved content. In knowledge paraphrasing, we fine-tune with multiple answers to the same question, enabling LLMs to better internalize specialized knowledge. To mitigate catastrophic forgetting due to fine-tuning, we add a domain-specific identifier to a question and also utilize a replay buffer containing general QA pairs. Experimental results demonstrate the efficacy of our method over existing techniques, achieving up to 10\% relative gain in token-level recall while preserving the LLM's generalization capabilities.

Related papers

• Oreo: A Plug-in Context Reconstructor to Enhance Retrieval-Augmented Generation [28.568010424711563]
  Large Language Models (LLMs) remain vulnerable to hallucinations due to their limited parametric knowledge and lack of domain-specific expertise. Retrieval-Augmented Generation (RAG) addresses this challenge by incorporating external document retrieval to augment the knowledge base of LLMs. We introduce a compact, efficient, and pluggable module designed to refine external knowledge sources before feeding them to the generator.
  arXiv  Detail & Related papers  (2025-02-18T16:38:39Z)
• Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentation [81.18701211912779]
  We introduce an Adaptive Multi-Aspect Retrieval-augmented over KGs (Amar) framework. This method retrieves knowledge including entities, relations, and subgraphs, and converts each piece of retrieved text into prompt embeddings. Our method has achieved state-of-the-art performance on two common datasets.
  arXiv  Detail & Related papers  (2024-12-24T16:38:04Z)
• Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented Generation [72.70046559930555]
  We propose a generic RAG approach called Adaptive Note-Enhanced RAG (Adaptive-Note) for complex QA tasks. Specifically, Adaptive-Note introduces an overarching view of knowledge growth, iteratively gathering new information in the form of notes. In addition, we employ an adaptive, note-based stop-exploration strategy to decide "what to retrieve and when to stop" to encourage sufficient knowledge exploration.
  arXiv  Detail & Related papers  (2024-10-11T14:03:29Z)
• Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization [7.522493227357079]
  Large Language Models (LLMs) are pre-trained on large-scale corpora. LLMs suffer from hallucinations, knowledge cut-offs, and lack of knowledge attributions. We introduce SMART-SLIC, a highly domain-specific LLM framework.
  arXiv  Detail & Related papers  (2024-10-03T17:40:55Z)
• CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Complex Knowledge Graph Question Answering [33.89497991289916]
  We propose a novel rewriting method CoTKR, Chain-of-Thought Enhanced Knowledge Rewriting, for generating reasoning traces and corresponding knowledge in an interleaved manner. We conduct experiments using various Large Language Models (LLMs) across several Knowledge Graph Question Answering (KGQA) benchmarks.
  arXiv  Detail & Related papers  (2024-09-29T16:08:45Z)
• REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering [115.72130322143275]
  REAR is a RElevance-Aware Retrieval-augmented approach for open-domain question answering (QA) We develop a novel architecture for LLM-based RAG systems, by incorporating a specially designed assessment module. Experiments on four open-domain QA tasks show that REAR significantly outperforms previous a number of competitive RAG approaches.
  arXiv  Detail & Related papers  (2024-02-27T13:22:51Z)
• 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)
• Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection [74.51523859064802]
  We introduce a new framework called Self-Reflective Retrieval-Augmented Generation (Self-RAG) Self-RAG enhances an LM's quality and factuality through retrieval and self-reflection. It significantly outperforms state-of-the-art LLMs and retrieval-augmented models on a diverse set of tasks.
  arXiv  Detail & Related papers  (2023-10-17T18:18:32Z)
• Self-Knowledge Guided Retrieval Augmentation for Large Language Models [59.771098292611846]
  Large language models (LLMs) have shown superior performance without task-specific fine-tuning. Retrieval-based methods can offer non-parametric world knowledge and improve the performance on tasks such as question answering. Self-Knowledge guided Retrieval augmentation (SKR) is a simple yet effective method which can let LLMs refer to the questions they have previously encountered.
  arXiv  Detail & Related papers  (2023-10-08T04:22:33Z)

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.