用智能体框架提升复杂关系抽取效果，尤其在低资源场景下表现优异。
AgentRE: An Agent-Based Framework for Navigating Complex Information Landscapes in Relation Extraction
- 构建具备记忆、检索与反思能力的智能体，分步处理复杂关系抽取任务
- 在中英文数据集上均显著优于传统模型，低资源场景下优势更明显
- 生成的推理轨迹可转化为高质量训练数据，用于微调小模型
复杂场景下的关系抽取面临关系类型多样、句内实体关系模糊等问题，导致纯“文本输入、文本输出”语言模型性能不佳。为此，本文提出基于智能体的关系抽取框架AgentRE，充分挖掘大语言模型在记忆、检索与反思方面的潜力，以应对复杂场景。AgentRE构建了三个核心模块，作为智能体获取和处理各类信息的工具，从而提升关系抽取性能。在英、中文两个数据集上的大量实验表明，AgentRE表现优越，尤其在低资源场景下。此外，AgentRE生成的推理轨迹可进一步优化，构建包含多种推理方法的高质量训练数据集，用于微调小型模型。代码已开源。
原文摘要 · Abstract (English)
The relation extraction (RE) in complex scenarios faces challenges such as diverse relation types and ambiguous relations between entities within a single sentence, leading to the poor performance of pure "text-in, text-out" language models (LMs). To address these challenges, in this paper, we propose an agent-based RE framework, namely AgentRE, which fully leverages the potential of large language models (LLMs) including memory, retrieval and reflection, to achieve RE in complex scenarios. Specifically, three major modules are built in AgentRE serving as the tools to help the agent acquire and process various useful information, thereby obtaining improved RE performance. Our extensive experimental results upon two datasets in English and Chinese demonstrate our AgentRE's superior performance, especially in low-resource scenarios. Additionally, the trajectories generated by AgentRE can be refined to construct a high-quality training dataset incorporating different reasoning methods, which can be used to fine-tune smaller models. Code is available at https://github.com/Lightblues/AgentRE.
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