基于大语言模型的垂直领域问答系统研究

Research on Vertical Domain Dialogue Systems Based on Large Language Model

垂直领域的任务型对话系统的构建存在缺乏标注数据、泛化性能差、无法冷启动的问题。最近,随着ChatGPT等大型语言模型的提出,自然语言处理领域有了很多新的进展。然而,对在探索大语言模型应用在垂直领域的任务型对话系统构建中的涌现能力的研究还较少。本文针对垂直领域任务型对话系统的构建难题,提出了基于大规模语言模型的意图和词槽识别方法,具体来说有三点:(1)微调大语言模型以提高意图和词槽识别性能;(2)采用多轮交互方式提升识别效果;(3)基于大模型生成训练数据进行数据增强。这些方法的综合应用,能够为垂直领域对话机器人的构建提供一个高效解决方案,减少对人工标注数据的依赖,提升对话机器人在few-shot和zero-shot情况下的准确性。

The construction of task-based dialogue systems for vertical domains suffers from lack of labeled data,poor generalization performance,and inability to start cold.Recently,with the proposal of large language models such as ChatGPT,there have been many new advances in the field of natural language processing.However,there are fewer studies exploring the emergent capabilities of large language models applied to the construction of task-based dialog systems in vertical domains.In this paper,we propose a approach for intent detection and slot filling based on large language models to address the challenges of building task-based dialog systems in vertical domains,specifically three points:(1)fine-tuning the large language models to improve the performance of intent detection and slot filling;(2)adopting multiple rounds of interactions with large language models to enhance the recognition effect;and(3)generating training data based on the large language models for data augmentation.The combined application of these methods can provide an efficient solution for the construction of dialog systems in vertical domains,reduce the reliance on manually labeled data,and improve the accuracy of dialog systems in fewshot and zero-shot situations.