M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models

• URL: http://arxiv.org/abs/2509.07730v2
• Date: Wed, 10 Sep 2025 04:50:56 GMT
• Title: M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models
• Authors: Zexuan Li, Hongliang Dai, Piji Li,
• Abstract summary: This paper proposes a framework called M-BRe to extract training instances from unlabeled texts for Relation Extraction (RE)<n>It utilizes three modules to combine the advantages of both of the above classification approaches: Relation Grouping, Relation Extraction, and Label Decision.<n>Extensive experiments confirm its superior capability in discovering high-quality training samples from unlabeled texts for RE.
• Score: 29.13677863733641
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For Relation Extraction (RE), the manual annotation of training data may be prohibitively expensive, since the sentences that contain the target relations in texts can be very scarce and difficult to find. It is therefore beneficial to develop an efficient method that can automatically extract training instances from unlabeled texts for training RE models. Recently, large language models (LLMs) have been adopted in various natural language processing tasks, with RE also benefiting from their advances. However, when leveraging LLMs for RE with predefined relation categories, two key challenges arise. First, in a multi-class classification setting, LLMs often struggle to comprehensively capture the semantics of every relation, leading to suboptimal results. Second, although employing binary classification for each relation individually can mitigate this issue, it introduces significant computational overhead, resulting in impractical time complexity for real-world applications. Therefore, this paper proposes a framework called M-BRe to extract training instances from unlabeled texts for RE. It utilizes three modules to combine the advantages of both of the above classification approaches: Relation Grouping, Relation Extraction, and Label Decision. Extensive experiments confirm its superior capability in discovering high-quality training samples from unlabeled texts for RE.

Related papers

• SPaRFT: Self-Paced Reinforcement Fine-Tuning for Large Language Models [51.74498855100541]
  Large language models (LLMs) have shown strong reasoning capabilities when fine-tuned with reinforcement learning (RL)<n>We propose textbfSPaRFT, a self-paced learning framework that enables efficient learning based on the capability of the model being trained.
  arXiv  Detail & Related papers  (2025-08-07T03:50:48Z)
• Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentation [81.18701211912779]
  We introduce an Adaptive Multi-Aspect Retrieval-augmented over KGs (Amar) framework.<n>This method retrieves knowledge including entities, relations, and subgraphs, and converts each piece of retrieved text into prompt embeddings.<n>Our method has achieved state-of-the-art performance on two common datasets.
  arXiv  Detail & Related papers  (2024-12-24T16:38:04Z)
• Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors [9.881102419679673]
  textscMicre (textbfMeta textbfIn-textbfContext learning of LLMs for textbfRelation textbfExtraction) is a new meta-training framework for zero and few-shot Relation extraction. We show that textscMicre can transfer the relation semantic knowledge via relation label name during inference on target RE datasets.
  arXiv  Detail & Related papers  (2024-04-27T07:06:39Z)
• Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction [33.528688487954454]
  Relation extraction (RE) aims to identify semantic relationships between entities within text. Few-shot learning, aiming to lessen annotation demands, typically provides incomplete and biased supervision for target relations. We introduce REPaL, comprising three stages: (1) We leverage large language models (LLMs) to generate initial seed instances from relation definitions and an unlabeled corpus.
  arXiv  Detail & Related papers  (2024-02-17T00:20:06Z)
• Revisiting Large Language Models as Zero-shot Relation Extractors [8.953462875381888]
  Relation extraction (RE) consistently involves a certain degree of labeled or unlabeled data even if under zero-shot setting. Recent studies have shown that large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt. This work focuses on the study of exploring LLMs as zero-shot relation extractors.
  arXiv  Detail & Related papers  (2023-10-08T06:17:39Z)
• Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data [43.46328487543664]
  Relation extraction (RE) aims to extract relations from sentences and documents. Recent studies showed that many RE datasets are incompletely annotated. This is known as the false negative problem in which valid relations are falsely annotated as 'no_relation'
  arXiv  Detail & Related papers  (2023-06-16T09:01:45Z)
• Description-Based Text Similarity [59.552704474862004]
  We identify the need to search for texts based on abstract descriptions of their content. We propose an alternative model that significantly improves when used in standard nearest neighbor search.
  arXiv  Detail & Related papers  (2023-05-21T17:14:31Z)
• BERM: Training the Balanced and Extractable Representation for Matching to Improve Generalization Ability of Dense Retrieval [54.66399120084227]
  We propose a novel method to improve the generalization of dense retrieval via capturing matching signal called BERM. Dense retrieval has shown promise in the first-stage retrieval process when trained on in-domain labeled datasets.
  arXiv  Detail & Related papers  (2023-05-18T15:43:09Z)
• Continual Contrastive Finetuning Improves Low-Resource Relation Extraction [34.76128090845668]
  Relation extraction has been particularly challenging in low-resource scenarios and domains. Recent literature has tackled low-resource RE by self-supervised learning. We propose to pretrain and finetune the RE model using consistent objectives of contrastive learning.
  arXiv  Detail & Related papers  (2022-12-21T07:30:22Z)
• Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study [51.33182775762785]
  This paper presents an empirical study to build relation extraction systems in low-resource settings. We investigate three schemes to evaluate the performance in low-resource settings: (i) different types of prompt-based methods with few-shot labeled data; (ii) diverse balancing methods to address the long-tailed distribution issue; and (iii) data augmentation technologies and self-training to generate more labeled in-domain data.
  arXiv  Detail & Related papers  (2022-10-19T15:46:37Z)
• Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction [52.63803634033647]
  Low-resource relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce. We develop a Gradient Imitation Reinforcement Learning method to encourage pseudo label data to imitate the gradient descent direction on labeled data. We also propose a framework called GradLRE, which handles two major scenarios in low-resource relation extraction.
  arXiv  Detail & Related papers  (2021-09-14T03:51:15Z)
• Learning to Ask Conversational Questions by Optimizing Levenshtein Distance [83.53855889592734]
  We introduce a Reinforcement Iterative Sequence Editing (RISE) framework that optimize the minimum Levenshtein distance (MLD) through explicit editing actions. RISE is able to pay attention to tokens that are related to conversational characteristics. Experimental results on two benchmark datasets show that RISE significantly outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-30T08:44:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.