AgentRE: An Agent-Based Framework for Navigating Complex Information Landscapes in Relation Extraction

• URL: http://arxiv.org/abs/2409.01854v1
• Date: Tue, 3 Sep 2024 12:53:05 GMT
• Title: AgentRE: An Agent-Based Framework for Navigating Complex Information Landscapes in Relation Extraction
• Authors: Yuchen Shi, Guochao Jiang, Tian Qiu, Deqing Yang,
• Abstract summary: relation extraction (RE) in complex scenarios faces challenges such as diverse relation types and ambiguous relations between entities within a single sentence. We propose an agent-based RE framework, namely AgentRE, which fully leverages the potential of large language models to achieve RE in complex scenarios.
• Score: 10.65417796726349
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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