一种通过知识挖掘与消化实现领域特定聊天机器人训练的自增强方法
A Self-enhancement Approach for Domain-specific Chatbot Training via Knowledge Mining and Digest
AI总结：
本文提出一种自增强方法，通过训练知识挖掘器LLMiner从领域文档中提取问答对，并结合对话数据微调LLM，以提升领域特定聊天机器人的性能，仅需600个种子实例即可实现显著改进。
摘要：
大型语言模型（LLMs）尽管在语言生成方面具有强大能力，但在处理特定领域中复杂且需要知识的查询时，常常遇到挑战。本文介绍了一种新颖的方法，通过有效提取领域特定文本源中的相关知识，并自适应训练聊天机器人以应对领域特定查询，从而增强大型语言模型。我们的两步方法首先训练一个知识挖掘器，即LLMiner，它通过链式思维推理过程从相关文档中自主提取问答对。随后，我们将挖掘出的问答对与对话数据集混合，对大型语言模型进行微调，使其成为聊天机器人，从而丰富其领域特定专业知识和对话能力。我们还开发了一个新的评估基准，该基准包含四个领域特定文本语料库及相应的人工构建的问答对用于测试。我们的模型在性能上显著优于一般对齐的大型语言模型，并超越了直接在领域语料库上微调的领域适应模型。特别是，LLMiner仅需600个种子实例即可实现这一目标，且只需最少的人工干预，从而为大型语言模型通过模型合成的训练数据实现自我改进提供了一条途径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
