Generating Diverse Training Samples for Relation Extraction with Large Language Models

• URL: http://arxiv.org/abs/2505.23108v1
• Date: Thu, 29 May 2025 05:21:54 GMT
• Title: Generating Diverse Training Samples for Relation Extraction with Large Language Models
• Authors: Zexuan Li, Hongliang Dai, Piji Li,
• Abstract summary: We study how to effectively improve the diversity of the training samples generated with Large Language Models (LLMs) for Relation Extraction (RE)<n>Our experiments on commonly used RE datasets show that both attempts can improve the quality of the generated training data.
• Score: 30.196619805354622
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Using Large Language Models (LLMs) to generate training data can potentially be a preferable way to improve zero or few-shot NLP tasks. However, many problems remain to be investigated for this direction. For the task of Relation Extraction (RE), we find that samples generated by directly prompting LLMs may easily have high structural similarities with each other. They tend to use a limited variety of phrasing while expressing the relation between a pair of entities. Therefore, in this paper, we study how to effectively improve the diversity of the training samples generated with LLMs for RE, while also maintaining their correctness. We first try to make the LLMs produce dissimilar samples by directly giving instructions in In-Context Learning (ICL) prompts. Then, we propose an approach to fine-tune LLMs for diversity training sample generation through Direct Preference Optimization (DPO). Our experiments on commonly used RE datasets show that both attempts can improve the quality of the generated training data. We also find that comparing with directly performing RE with an LLM, training a non-LLM RE model with its generated samples may lead to better performance.

Related papers

• Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting [21.04933334040135]
  We introduce the Self-Prompting framework, a novel method designed to fully harness the embedded RE knowledge within Large Language Models.<n>Our framework employs a three-stage diversity approach to prompt LLMs, generating multiple synthetic samples that encapsulate specific relations from scratch.<n> Experimental evaluations on benchmark datasets show our approach outperforms existing LLM-based zero-shot RE methods.
  arXiv  Detail & Related papers  (2024-10-02T01:12:54Z)
• LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs? [2.7820774076399957]
  We compare effects of recent LLM augmentation methods with established ones on 6 datasets, 3 classifiers and 2 fine-tuning methods. We show that LLM-based methods are worthy of deployment only when very small number of seeds is used.
  arXiv  Detail & Related papers  (2024-08-29T13:01:42Z)
• Entropy Law: The Story Behind Data Compression and LLM Performance [115.70395740286422]
  We find that model performance is negatively correlated to the compression ratio of training data, which usually yields a lower training loss. Based on the findings of the entropy law, we propose a quite efficient and universal data selection method. We also present an interesting application of entropy law that can detect potential performance risks at the beginning of model training.
  arXiv  Detail & Related papers  (2024-07-09T08:14:29Z)
• ITERTL: An Iterative Framework for Fine-tuning LLMs for RTL Code Generation [9.409062607311528]
  Large language models (LLMs) have demonstrated excellent performance, inspiring researchers to explore their use in automating register transfer level (RTL) code generation.<n>Existing approaches to fine-tune LLMs for RTL generation typically are conducted on fixed datasets.<n>We introduce an iterative training paradigm named ITERTL to mitigate these issues.<n>Our model outperforms GPT4 and state-of-the-art (SOTA) open-source models, achieving remarkable 53.8% pass@1 rate on VerilogEval-human benchmark.
  arXiv  Detail & Related papers  (2024-06-28T01:44:57Z)
• Aligning Language Models with Demonstrated Feedback [58.834937450242975]
  Demonstration ITerated Task Optimization (DITTO) directly aligns language model outputs to a user's demonstrated behaviors.<n>We evaluate DITTO's ability to learn fine-grained style and task alignment across domains such as news articles, emails, and blog posts.
  arXiv  Detail & Related papers  (2024-06-02T23:13:56Z)
• PGA-SciRE: Harnessing LLM on Data Augmentation for Enhancing Scientific Relation Extraction [3.115124630982566]
  Relation Extraction (RE) aims at recognizing the relation between pairs of entities mentioned in a text. We propose a framework called PGA for improving the performance of models for RE in the scientific domain.
  arXiv  Detail & Related papers  (2024-05-30T13:07:54Z)
• Recall, Retrieve and Reason: Towards Better In-Context Relation Extraction [11.535892987373947]
  Relation extraction (RE) aims to identify relations between entities mentioned in texts. Large language models (LLMs) have demonstrated impressive in-context learning abilities in various tasks. LLMs suffer from poor performances compared to most supervised fine-tuned RE methods.
  arXiv  Detail & Related papers  (2024-04-27T07:12:52Z)
• Alpaca against Vicuna: Using LLMs to Uncover Memorization of LLMs [61.04246774006429]
  We introduce a black-box prompt optimization method that uses an attacker LLM agent to uncover higher levels of memorization in a victim agent.<n>We observe that our instruction-based prompts generate outputs with 23.7% higher overlap with training data compared to the baseline prefix-suffix measurements.<n>Our findings show that instruction-tuned models can expose pre-training data as much as their base-models, if not more so, and using instructions proposed by other LLMs can open a new avenue of automated attacks.
  arXiv  Detail & Related papers  (2024-03-05T19:32:01Z)
• Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization [165.98557106089777]
  A key challenge is to enhance the capabilities of large language models (LLMs) amid a looming shortage of high-quality training data. Our study starts from an empirical strategy for the light continual training of LLMs using their original pre-training data sets. We then formalize this strategy into a principled framework of Instance-Reweighted Distributionally Robust Optimization.
  arXiv  Detail & Related papers  (2024-02-22T04:10:57Z)
• Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning [79.32236399694077]
  Low-quality data in the training set are usually detrimental to instruction tuning. We propose a novel method, termed "reflection-tuning" This approach utilizes an oracle LLM to recycle the original training data by introspecting and enhancing the quality of instructions and responses in the data.
  arXiv  Detail & Related papers  (2023-10-18T05:13:47Z)
• 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)
• On Learning to Summarize with Large Language Models as References [101.79795027550959]
  Large language models (LLMs) are favored by human annotators over the original reference summaries in commonly used summarization datasets. We study an LLM-as-reference learning setting for smaller text summarization models to investigate whether their performance can be substantially improved.
  arXiv  Detail & Related papers  (2023-05-23T16:56:04Z)

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.