基于检索增强生成的大语言模型用于因果挖掘
Retrieval Augmented Generation based Large Language Models for Causality Mining
AI总结：
本文提出基于检索增强生成（RAG）的动态提示方案，提升大语言模型在因果检测与挖掘任务中的性能，并在多数据集和多模型上验证其优越性。
摘要：
因果检测与挖掘在信息检索中是重要任务，因其在信息抽取和知识图谱构建中有广泛应用。为解决这些任务，现有文献中存在多种解决方案——包括无监督和有监督方法。然而，无监督方法性能较差，且常需大量人工干预以选择因果规则，导致在不同领域间泛化能力不足。另一方面，有监督方法则受限于缺乏大规模训练数据。近期研究发现，采用有效提示工程的大语言模型（LLMs）能够有效应对训练数据不足的问题。然而，现有文献中尚无利用LLM提示进行因果检测与挖掘的综合性研究。本文提出多种基于检索增强生成（RAG）的动态提示方案，以提升LLM在因果检测与抽取任务中的表现。在三个数据集和五个LLM上的大量实验验证了所提基于RAG的动态提示相较其他静态提示方案的优越性。
英文摘要：
Causality detection and mining are important tasks in information retrieval due to their enormous use in information extraction, and knowledge graph construction. To solve these tasks, in existing literature there exist several solutions -- both unsupervised and supervised. However, the unsupervised methods suffer from poor performance and they often require significant human intervention for causal rule selection, leading to poor generalization across different domains. On the other hand, supervised methods suffer from the lack of large training datasets. Recently, large language models (LLMs) with effective prompt engineering are found to be effective to overcome the issue of unavailability of large training dataset. Yet, in existing literature, there does not exist comprehensive works on causality detection and mining using LLM prompting. In this paper, we present several retrieval-augmented generation (RAG) based dynamic prompting schemes to enhance LLM performance in causality detection and extraction tasks. Extensive experiments over three datasets and five LLMs validate the superiority of our proposed RAG-based dynamic prompting over other static prompting schemes.
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