A Self-enhancement Approach for Domain-specific Chatbot Training via Knowledge Mining and Digest

• URL: http://arxiv.org/abs/2311.10614v1
• Date: Fri, 17 Nov 2023 16:09:10 GMT
• Title: A Self-enhancement Approach for Domain-specific Chatbot Training via Knowledge Mining and Digest
• Authors: Ruohong Zhang, Luyu Gao, Chen Zheng, Zhen Fan, Guokun Lai, Zheng Zhang, Fangzhou Ai, Yiming Yang, Hongxia Yang
• Abstract summary: Large Language Models (LLMs) often encounter challenges when dealing with intricate and knowledge-demanding queries in specific domains. This paper introduces a novel approach to enhance LLMs by effectively extracting the relevant knowledge from domain-specific textual sources. We train a knowledge miner, namely LLMiner, which autonomously extracts Question-Answer pairs from relevant documents.
• Score: 62.63606958140248
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs), despite their great power in language generation, often encounter challenges when dealing with intricate and knowledge-demanding queries in specific domains. This paper introduces a novel approach to enhance LLMs by effectively extracting the relevant knowledge from domain-specific textual sources, and the adaptive training of a chatbot with domain-specific inquiries. Our two-step approach starts from training a knowledge miner, namely LLMiner, which autonomously extracts Question-Answer pairs from relevant documents through a chain-of-thought reasoning process. Subsequently, we blend the mined QA pairs with a conversational dataset to fine-tune the LLM as a chatbot, thereby enriching its domain-specific expertise and conversational capabilities. We also developed a new evaluation benchmark which comprises four domain-specific text corpora and associated human-crafted QA pairs for testing. Our model shows remarkable performance improvement over generally aligned LLM and surpasses domain-adapted models directly fine-tuned on domain corpus. In particular, LLMiner achieves this with minimal human intervention, requiring only 600 seed instances, thereby providing a pathway towards self-improvement of LLMs through model-synthesized training data.

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