Year: 2026
Type: conference-paper
Abstract: Relation extraction is a fundamental task in information extraction.Still, existing supervised approaches rely heavily on largescale annotated data, limiting their applicability in domain-specific and low-resource scenarios.Prompt-based methods with large language models provide a parameter-efficient alternative; however, their performance is susceptible to prompt design, which often requires extensive domain expertise and heuristic trial-and-error.We propose REPO, a reinforcement learning-based automated prompt optimization framework for domain relation extraction.REPO formulates prompt construction as a structured, sequential decisionmaking problem, optimizing prompt quality through interaction with a black-box LLM.To enable efficient and stable optimization, we introduce a two-stage framework comprising an initial prompt-construction stage that generates semantically grounded candidates and a DRL-based refinement stage that iteratively improves prompts within a constrained, domain-aware action space.We further design a composite evaluation metric that integrates extraction accuracy and semantic consistency to serve as a dense reward signal.Extensive experiments on multiple relation extraction datasets across medical, financial, legal, and news domains demonstrate that REPO consistently outperforms existing prompt-based methods and supervised baselines.Ablation studies further confirm the effectiveness and robustness of the proposed DRL-based prompt optimization strategy.
Authors: Ying Liu, Dong Shuai, Cui Zibo, TengQi Ye, Gang Wu
Institutions: Northeastern University, Meta (United States)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166814632