检索增强生成框架中基于微调大语言模型的关系抽取
Relation Extraction with Fine-Tuned Large Language Models in Retrieval Augmented Generation Frameworks
AI总结：
本研究将微调后的大语言模型集成到检索增强生成（RAG）框架中开展关系抽取，解决句子级隐式关系识别难题，在多个基准数据集上取得超越既往方法的性能表现。
摘要：
信息抽取（IE）对于将非结构化数据转换为知识图谱（KGs）等结构化格式至关重要。关系抽取（RE）是信息抽取中的一项核心任务，旨在识别文本中实体之间的关系。现有的关系抽取方法多种多样，包括监督式、无监督式、弱监督式以及基于规则的方法。近期利用预训练语言模型（PLMs）的研究在该领域取得了显著成效。在当前大语言模型（LLMs）主导的时代，对这些模型进行微调能够克服基于零样本提示的大语言模型关系抽取方法的局限性，尤其是在领域适应挑战以及识别句子中实体间隐式关系方面的局限。这些隐式关系无法轻易从句子的依存树中提取，需要逻辑推理才能准确识别。本研究探索了微调后的大语言模型的性能，以及将其集成到基于检索增强生成（RAG）的关系抽取方法中的效果，以解决句子级隐式关系识别的挑战，特别是当大语言模型在RAG框架中作为生成器时的情况。在TACRED、TACRED-Revisited（TACREV）、Re-TACRED和SemEVAL数据集上的实证评估表明，使用Llama2-7B、Mistral-7B和T5（Large）等微调后的大语言模型可带来显著的性能提升。值得注意的是，我们的方法在隐式关系普遍存在的SemEVAL数据集上取得了大幅增益，超越了该数据集上的既往结果。此外，我们的方法在TACRED、TACREV和Re-TACRED数据集上的表现也优于先前研究，证明了其在多种评估场景下的优异性能。
英文摘要：
Information Extraction (IE) is crucial for converting unstructured data into structured formats like Knowledge Graphs (KGs). A key task within IE is Relation Extraction (RE), which identifies relationships between entities in text. Various RE methods exist, including supervised, unsupervised, weakly supervised, and rule-based approaches. Recent studies leveraging pre-trained language models (PLMs) have shown significant success in this area. In the current era dominated by Large Language Models (LLMs), fine-tuning these models can overcome limitations associated with zero-shot LLM prompting-based RE methods, especially regarding domain adaptation challenges and identifying implicit relations between entities in sentences. These implicit relations, which cannot be easily extracted from a sentence's dependency tree, require logical inference for accurate identification. This work explores the performance of fine-tuned LLMs and their integration into the Retrieval Augmented-based (RAG) RE approach to address the challenges of identifying implicit relations at the sentence level, particularly when LLMs act as generators within the RAG framework. Empirical evaluations on the TACRED, TACRED-Revisited (TACREV), Re-TACRED, and SemEVAL datasets show significant performance improvements with fine-tuned LLMs, including Llama2-7B, Mistral-7B, and T5 (Large). Notably, our approach achieves substantial gains on SemEVAL, where implicit relations are common, surpassing previous results on this dataset. Additionally, our method outperforms previous works on TACRED, TACREV, and Re-TACRED, demonstrating exceptional performance across diverse evaluation scenarios.
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