用动态检索增强生成提升大模型因果关系挖掘能力
Retrieval Augmented Generation based Large Language Models for Causality Mining
- 设计基于检索增强的动态提示策略，动态获取相关上下文
- 在三个数据集上验证，优于传统静态提示方法
- 适合需要高精度因果关系抽取的研究者使用
因果关系检测与挖掘在信息检索中至关重要，广泛应用于信息抽取和知识图谱构建。现有方法包括无监督和有监督两类：无监督方法性能差且需大量人工干预选择因果规则，泛化能力弱；有监督方法受限于缺乏大规模训练数据。近年来，大语言模型（LLMs）结合有效提示工程被证明可缓解数据不足问题，但现有研究尚未系统探索基于提示的因果挖掘方法。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在因果关系检测与提取任务中的表现。在三个数据集和五种LLMs上的实验表明，所提RAG动态提示方法显著优于其他静态提示策略。
原文摘要 · Abstract (English)
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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