Efficient Tuning of Large Language Models for Knowledge-Grounded Dialogue Generation

• URL: http://arxiv.org/abs/2504.07754v1
• Date: Thu, 10 Apr 2025 13:54:36 GMT
• Title: Efficient Tuning of Large Language Models for Knowledge-Grounded Dialogue Generation
• Authors: Bo Zhang, Hui Ma, Dailin Li, Jian Ding, Jian Wang, Bo Xu, HongFei Lin,
• Abstract summary: We introduce KEDiT, an efficient method for fine-tuning large language models for knowledge-grounded dialogue generation.<n> KEDiT operates in two main phases: first, it employs an information bottleneck to compress retrieved knowledge into learnable parameters, retaining essential information while minimizing computational overhead.<n> experimental results on the Wizard of Wikipedia and a newly constructed PubMed-Dialog dataset demonstrate that KEDiT excels in generating contextually relevant and informative responses.
• Score: 21.52726424882653
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) demonstrate remarkable text comprehension and generation capabilities but often lack the ability to utilize up-to-date or domain-specific knowledge not included in their training data. To address this gap, we introduce KEDiT, an efficient method for fine-tuning LLMs for knowledge-grounded dialogue generation. KEDiT operates in two main phases: first, it employs an information bottleneck to compress retrieved knowledge into learnable parameters, retaining essential information while minimizing computational overhead. Second, a lightweight knowledge-aware adapter integrates these compressed knowledge vectors into the LLM during fine-tuning, updating less than 2\% of the model parameters. The experimental results on the Wizard of Wikipedia and a newly constructed PubMed-Dialog dataset demonstrate that KEDiT excels in generating contextually relevant and informative responses, outperforming competitive baselines in automatic, LLM-based, and human evaluations. This approach effectively combines the strengths of pretrained LLMs with the adaptability needed for incorporating dynamic knowledge, presenting a scalable solution for fields such as medicine.

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