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GPT-RE: In-context Learning for Relation Extraction using Large Language Models

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arxiv 2305.02105 v3 pith:L7HVD6RQ submitted 2023-05-03 cs.CL

Zhen Wan , Fei Cheng , Zhuoyuan Mao , Qianying Liu , Haiyue Song , Jiwei Li , Sadao Kurohashi This is my paper
classification cs.CL
keywords gpt-rebaselinesdatasetsfully-supervisedllmsrelationachievesdemonstrations
verification ladder T0 review T1 audit T2 compute T3 formal
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In spite of the potential for ground-breaking achievements offered by large language models (LLMs) (e.g., GPT-3), they still lag significantly behind fully-supervised baselines (e.g., fine-tuned BERT) in relation extraction (RE). This is due to the two major shortcomings of LLMs in RE: (1) low relevance regarding entity and relation in retrieved demonstrations for in-context learning; and (2) the strong inclination to wrongly classify NULL examples into other pre-defined labels. In this paper, we propose GPT-RE to bridge the gap between LLMs and fully-supervised baselines. GPT-RE successfully addresses the aforementioned issues by (1) incorporating task-specific entity representations in demonstration retrieval; and (2) enriching the demonstrations with gold label-induced reasoning logic. We evaluate GPT-RE on four widely-used RE datasets, and observe that GPT-RE achieves improvements over not only existing GPT-3 baselines, but also fully-supervised baselines. Specifically, GPT-RE achieves SOTA performances on the Semeval and SciERC datasets, and competitive performances on the TACRED and ACE05 datasets.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    EmpiriGraph-Psy supplies a new benchmark dataset and staged LLM pipeline for variable-centered empirical graph extraction from psychology abstracts, outperforming direct extraction at 0.74 macro-F1.

  2. Large Multi-modal Model Cartographic Map Comprehension for Textual Locality Georeferencing

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    Grid-based prompting lets GPT-4o georeference textual locality descriptions from natural history records using map excerpts, averaging about 1.03 km error on a 25-example dataset.

  3. R1-RE: Cross-Domain Relation Extraction with RLVR

    cs.CL 2025-07 conditional novelty 6.0 of 10

    R1-RE uses GRPO reinforcement learning with format and accuracy rewards to make a 7B model reason through annotation guidelines, improving cross-domain relation classification.

  4. Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval

    cs.CL 2025-06 conditional novelty 6.0 of 10

    Syntactic similarity retrieval of demonstrations improves LLM-based automatic term extraction in cross-domain settings, but gains are modest and in-domain lexical retrieval is often competitive or better.

  5. Large Language Model for Extracting Complex Contract Information in Industrial Scenes

    cs.CL 2025-07 conditional novelty 3.0 of 10

    Clustering contracts, LLM-based labeling, augmentation, and LoRA fine-tuning improve Chinese industrial contract field extraction over traditional TF-IDF/TextRank/SNOWNLP/KeyBERT baselines.

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