Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction

• URL: http://arxiv.org/abs/2402.11142v2
• Date: Fri, 25 Oct 2024 03:37:12 GMT
• Title: Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction
• Authors: Sizhe Zhou, Yu Meng, Bowen Jin, Jiawei Han,
• Abstract summary: 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.
• Score: 33.528688487954454
• License:
• Abstract: Relation extraction (RE) aims to identify semantic relationships between entities within text. Despite considerable advancements, existing models predominantly require extensive annotated training data, which is both costly and labor-intensive to collect. Moreover, these models often struggle to adapt to new or unseen relations. Few-shot learning, aiming to lessen annotation demands, typically provides incomplete and biased supervision for target relations, leading to degraded and unstable performance. To accurately and explicitly describe relation semantics while minimizing annotation demands, we explore the definition only zero-shot RE setting where only relation definitions expressed in natural language are used to train a RE model. 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. (2) We fine-tune a bidirectional Small Language Model (SLM) with initial seeds to learn relations for the target domain. (3) We expand pattern coverage and mitigate bias from initial seeds by integrating feedback from the SLM's predictions on the unlabeled corpus and the synthesis history. To accomplish this, we leverage the multi-turn conversation ability of LLMs to generate new instances in follow-up dialogues, informed by both the feedback and synthesis history. Studies reveal that definition-oriented seed synthesis enhances pattern coverage whereas indiscriminately increasing seed quantity leads to performance saturation. Experiments on two datasets show REPaL significantly improved cost-effective zero-shot performance by large margins.

Related papers

• Relation Extraction with Fine-Tuned Large Language Models in Retrieval Augmented Generation Frameworks [0.0]
  Relation Extraction (RE) is crucial for converting unstructured data into structured formats like Knowledge Graphs (KGs) Recent studies leveraging pre-trained language models (PLMs) have shown significant success in this area. This work explores the performance of fine-tuned LLMs and their integration into the Retrieval Augmented-based (RAG) RE approach.
  arXiv  Detail & Related papers  (2024-06-20T21:27:57Z)
• Factual Dialogue Summarization via Learning from Large Language Models [35.63037083806503]
  Large language model (LLM)-based automatic text summarization models generate more factually consistent summaries. We employ zero-shot learning to extract symbolic knowledge from LLMs, generating factually consistent (positive) and inconsistent (negative) summaries. Our approach achieves better factual consistency while maintaining coherence, fluency, and relevance, as confirmed by various automatic evaluation metrics.
  arXiv  Detail & Related papers  (2024-06-20T20:03:37Z)
• Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery [12.716874398564482]
  Generalized Relation Discovery (GRD) aims to identify unlabeled instances in existing pre-defined relations or discover novel relations. We propose a novel framework, SFGRD, for this task by learning from semi-factuals in two stages. SFGRD surpasses state-of-the-art models in terms of accuracy by 2.36% $sim$5.78% and cosine similarity by 32.19%$sim$ 84.45%.
  arXiv  Detail & Related papers  (2024-01-12T02:38:55Z)
• Exploiting Contextual Target Attributes for Target Sentiment Classification [53.30511968323911]
  Existing PTLM-based models for TSC can be categorized into two groups: 1) fine-tuning-based models that adopt PTLM as the context encoder; 2) prompting-based models that transfer the classification task to the text/word generation task. We present a new perspective of leveraging PTLM for TSC: simultaneously leveraging the merits of both language modeling and explicit target-context interactions via contextual target attributes.
  arXiv  Detail & Related papers  (2023-12-21T11:45:28Z)
• Large Language Models with Controllable Working Memory [64.71038763708161]
  Large language models (LLMs) have led to a series of breakthroughs in natural language processing (NLP) What further sets these models apart is the massive amounts of world knowledge they internalize during pretraining. How the model's world knowledge interacts with the factual information presented in the context remains under explored.
  arXiv  Detail & Related papers  (2022-11-09T18:58:29Z)
• Improving Distantly Supervised Relation Extraction by Natural Language Inference [9.181270251524866]
  We propose a novel DSRE-NLI framework, which considers both distant supervision from existing knowledge bases and indirect supervision from pretrained language models for other tasks. DSRE-NLI energizes an off-the-shelf natural language inference (NLI) engine with a semi-automatic relation verbalization (SARV) mechanism to provide indirect supervision. With two simple and effective data consolidation strategies, the quality of training data is substantially improved.
  arXiv  Detail & Related papers  (2022-07-31T02:48:34Z)
• RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction [65.4337085607711]
  We introduce the task setting of Zero-Shot Relation Triplet Extraction (ZeroRTE) Given an input sentence, each extracted triplet consists of the head entity, relation label, and tail entity where the relation label is not seen at the training stage. We propose to synthesize relation examples by prompting language models to generate structured texts.
  arXiv  Detail & Related papers  (2022-03-17T05:55:14Z)
• Automatically Generating Counterfactuals for Relation Exaction [18.740447044960796]
  relation extraction (RE) is a fundamental task in natural language processing. Current deep neural models have achieved high accuracy but are easily affected by spurious correlations. We develop a novel approach to derive contextual counterfactuals for entities.
  arXiv  Detail & Related papers  (2022-02-22T04:46:10Z)
• SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction [51.27558374091491]
  We propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for relation extraction. Based on a broad spectrum of carefully designed tasks, our proposed SAIS method not only extracts relations of better quality due to more effective supervision, but also retrieves the corresponding supporting evidence more accurately.
  arXiv  Detail & Related papers  (2021-09-24T17:37:35Z)
• Syntax-Enhanced Pre-trained Model [49.1659635460369]
  We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. We present a model that utilizes the syntax of text in both pre-training and fine-tuning stages.
  arXiv  Detail & Related papers  (2020-12-28T06:48:04Z)
• Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction [84.64435075778988]
  We propose a general approach to learn relation prototypes from unlabeled texts. We learn relation prototypes as an implicit factor between entities. We conduct experiments on two publicly available datasets: New York Times and Google Distant Supervision.
  arXiv  Detail & Related papers  (2020-11-27T06:21:12Z)

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.