DeTri: Debiasing General-Purpose LLMs for Zero-Shot Relation Triplet Extraction via Structural Expert
TL;DR
DETRI is introduced, a novel debiasing framework for ZSRTE that addresses biases by leveraging a discriminative structural expert model and shows strong generalization across different LLMs, offering a solution to mitigate biases.
Abstract
Zero-Shot Relation Triplet Extraction (ZSRTE) aims to extract relation triplets for unseen relation types without any annotated training data. Recent advancements in Large Language Models (LLMs) have significantly enhanced ZSRTE performance, enabling the direct generation of relational triplets from unstructured text. However, LLMs often introduce biases such as entity shift, relation confusion, and over-prediction, which limit the reliability of the extracted triplets. In this paper, we introduce DETRI, a novel debiasing framework for ZSRTE that addresses these biases by leveraging a discriminative structural expert model. DETRI treats LLMs as inspiration generators and refines their outputs via a three-stage debiasing pipeline, consisting of inspiration-based re-prediction, confidence-based filtering, and entity shift correction through span perturbation. The framework improves LLM-generated triplets without requiring fine-tuning the LLM, thus offering a transferable and scalable solution. Extensive experiments on two datasets show that DETRI outperforms state-of-the-art methods by reducing bias and improving extraction accuracy, achieving 2.89% F1 improvement over fine-tuned LLM methods while keeping the LLM parameters frozen. Our approach shows strong generalization across different LLMs, offering a solution to mitigate biases.