Papers › LLMQuoter: Enhancing RAG Capabilities Through Efficient Quote Extraction From Large Contexts

LLMQuoter: Enhancing RAG Capabilities Through Efficient Quote Extraction From Large Contexts

9 Jan 2025arXiv:2501.05554archive 2025-07-28

Yuri Facanha Bezerra, Li Weigang

We introduce LLMQuoter, a lightweight, distillation-based model designed to enhance Retrieval Augmented Generation (RAG) by extracting the most relevant textual evidence for downstream reasoning tasks. Built on the LLaMA-3B architecture and fine-tuned with Low-Rank Adaptation (LoRA) on a 15,000-sample subset of HotpotQA, LLMQuoter adopts a "quote-first-then-answer" strategy, efficiently identifying key quotes before passing curated snippets to reasoning models. This workflow reduces cognitive overhead and outperforms full-context approaches like Retrieval-Augmented Fine-Tuning (RAFT), achieving over 20-point accuracy gains across both small and large language models. By leveraging knowledge distillation from a high-performing teacher model, LLMQuoter achieves competitive results in a resource-efficient fine-tuning setup. It democratizes advanced RAG capabilities, delivering significant performance improvements without requiring extensive model retraining. Our results highlight the potential of distilled quote-based reasoning to streamline complex workflows, offering a scalable and practical solution for researchers and practitioners alike.

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Knowledge Distillation RAG Retrieval Retrieval-augmented Generation

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Adam Attention Attention Dropout BART BERT BPE Dense Connections Dropout Knowledge Distillation Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention RAG Residual Connection Softmax Weight Decay WordPiece

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