Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting

• URL: http://arxiv.org/abs/2410.01154v1
• Date: Wed, 2 Oct 2024 01:12:54 GMT
• Title: Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting
• Authors: Siyi Liu, Yang Li, Jiang Li, Shan Yang, Yunshi Lan,
• Abstract summary: We introduce the Self-Prompting framework, a novel method designed to fully harness the embedded RE knowledge within Large Language Models. Our framework employs a three-stage diversity approach to prompt LLMs, generating multiple synthetic samples that encapsulate specific relations from scratch. Experimental evaluations on benchmark datasets show our approach outperforms existing LLM-based zero-shot RE methods.
• Score: 21.04933334040135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent research in zero-shot Relation Extraction (RE) has focused on using Large Language Models (LLMs) due to their impressive zero-shot capabilities. However, current methods often perform suboptimally, mainly due to a lack of detailed, context-specific prompts needed for understanding various sentences and relations. To address this, we introduce the Self-Prompting framework, a novel method designed to fully harness the embedded RE knowledge within LLMs. Specifically, our framework employs a three-stage diversity approach to prompt LLMs, generating multiple synthetic samples that encapsulate specific relations from scratch. These generated samples act as in-context learning samples, offering explicit and context-specific guidance to efficiently prompt LLMs for RE. Experimental evaluations on benchmark datasets show our approach outperforms existing LLM-based zero-shot RE methods. Additionally, our experiments confirm the effectiveness of our generation pipeline in producing high-quality synthetic data that enhances performance.

Related papers

• Empowering Few-Shot Relation Extraction with The Integration of Traditional RE Methods and Large Language Models [48.846159555253834]
  Few-Shot Relation Extraction (FSRE) appeals to more researchers in Natural Language Processing (NLP) Recent emergence of Large Language Models (LLMs) has prompted numerous researchers to explore FSRE through In-Context Learning (ICL)
  arXiv  Detail & Related papers  (2024-07-12T03:31:11Z)
• ITERTL: An Iterative Framework for Fine-tuning LLMs for RTL Code Generation [9.409062607311528]
  Large language models (LLMs) have demonstrated excellent performance in understanding human instructions and generating code. We introduce a simple yet effective iterative training paradigm named ITERTL. We show the model trained through our proposed approach can compete with and even outperform the state-of-the-art (SOTA) open-source model.
  arXiv  Detail & Related papers  (2024-06-28T01:44:57Z)
• Improve Temporal Awareness of LLMs for Sequential Recommendation [61.723928508200196]
  Large language models (LLMs) have demonstrated impressive zero-shot abilities in solving a wide range of general-purpose tasks. LLMs fall short in recognizing and utilizing temporal information, rendering poor performance in tasks that require an understanding of sequential data. We propose three prompting strategies to exploit temporal information within historical interactions for LLM-based sequential recommendation.
  arXiv  Detail & Related papers  (2024-05-05T00:21:26Z)
• C-ICL: Contrastive In-context Learning for Information Extraction [54.39470114243744]
  c-ICL is a novel few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations. Our experiments on various datasets indicate that c-ICL outperforms previous few-shot in-context learning methods.
  arXiv  Detail & Related papers  (2024-02-17T11:28:08Z)
• ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks [91.55895047448249]
  This paper presents ReEval, an LLM-based framework using prompt chaining to perturb the original evidence for generating new test cases. We implement ReEval using ChatGPT and evaluate the resulting variants of two popular open-domain QA datasets. Our generated data is human-readable and useful to trigger hallucination in large language models.
  arXiv  Detail & Related papers  (2023-10-19T06:37:32Z)
• 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)
• Summarization is (Almost) Dead [49.360752383801305]
  We develop new datasets and conduct human evaluation experiments to evaluate the zero-shot generation capability of large language models (LLMs) Our findings indicate a clear preference among human evaluators for LLM-generated summaries over human-written summaries and summaries generated by fine-tuned models.
  arXiv  Detail & Related papers  (2023-09-18T08:13:01Z)
• ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation [43.270424225285105]
  We focus on adapting and empowering a pure large language model for zero-shot and few-shot recommendation tasks. We propose Retrieval-enhanced Large Language models (ReLLa) for recommendation tasks in both zero-shot and few-shot settings.
  arXiv  Detail & Related papers  (2023-08-22T02:25:04Z)
• 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)

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.