Efficient Domain-Specific Chinese Relation Extraction with Fine-tuned Large Language Models

Core Concepts

CRE-LLM, a framework for domain-specific Chinese relation extraction, leverages fine-tuned open-source large language models to directly and efficiently extract relations between entities in unstructured Chinese text.

Abstract

The paper introduces CRE-LLM, a novel framework for domain-specific Chinese relation extraction (DSCRE) that utilizes fine-tuned open-source large language models (LLMs) to directly extract relations between entities in unstructured Chinese text. The key highlights are: CRE-LLM addresses the challenges of complex network design, poor internal perception, and high fine-tuning costs faced by previous DSCRE methods. It employs a simple and direct generative approach by fine-tuning open-source LLMs like Llama-2, ChatGLM2, and Baichuan2. The framework constructs an appropriate prompt based on the given entities and text, and then fine-tunes the LLMs using the Parameter-Efficient Fine-Tuning (PEFT) framework. This enhances the logical awareness and generation capabilities of the model for DSCRE tasks. Extensive experiments on two domain-specific DSCRE datasets, FinRE and SanWen, demonstrate that CRE-LLM outperforms existing methods and achieves state-of-the-art performance on the FinRE dataset. The fine-tuning approach using PEFT significantly reduces the memory consumption and environmental configuration requirements compared to full fine-tuning, making CRE-LLM more accessible for general projects and teams. Error analysis reveals that the primary challenges lie in understanding entity relations, handling multiple relations, and correctly identifying the "NA" relation, which the authors aim to address in future work. Overall, CRE-LLM represents a promising direction for applying powerful LLMs to domain-specific relation extraction tasks in a simple, efficient, and effective manner.

Stats

"With the establishment of [Ant Financial], [Alibaba]'s layout in the financial business has been officially clarified." "内部人士昨日透露，[双汇国际]内部对于"双币双股"这种模式上市还没有完全确定。"

Quotes

"CRE-LLM enhances the logic-awareness and generative capabilities of the model by constructing an appropriate prompt and utilizing open-source LLMs for instruction-supervised fine-tuning." "The experimental results show that CRE-LLM is significantly superior and robust, achieving state-of-the-art (SOTA) performance on the FinRE dataset."

Key Insights Distilled From

CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language Model

by Zhengpeng Sh... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18085.pdf

Deeper Inquiries

How can the CRE-LLM framework be extended to handle more complex relation types, such as n-ary relations or relations with attributes?

In order to extend the CRE-LLM framework to handle more complex relation types, such as n-ary relations or relations with attributes, several enhancements can be considered: Prompt Design: The prompt design in the instruction-supervised fine-tuning process can be modified to include specific cues or markers for different types of relations. For n-ary relations, the prompt can be structured to guide the model on how to identify and extract relations involving multiple entities. For relations with attributes, the prompt can provide instructions on recognizing and extracting attributes associated with the relations. Dataset Augmentation: To train the model effectively on a wider range of relation types, the training dataset can be augmented with examples of n-ary relations and relations with attributes. This will expose the model to diverse patterns and variations in relation structures, enabling it to learn more complex relationships. Fine-Tuning Strategies: Fine-tuning strategies can be optimized to focus on capturing the nuances of n-ary relations and relations with attributes. This may involve adjusting the learning rate, batch size, or other hyperparameters to enhance the model's ability to extract complex relations accurately. Model Architecture: The architecture of the LLM can be customized or adapted to better handle the complexities of n-ary relations and relations with attributes. This may involve incorporating additional attention mechanisms or specialized modules to capture the intricate dependencies between entities and attributes in the text. By incorporating these enhancements, the CRE-LLM framework can be extended to effectively handle more complex relation types, providing a more comprehensive solution for domain-specific relation extraction tasks.

How can the potential limitations of the instruction-supervised fine-tuning approach be further improved to address the challenges identified in the error analysis?

To address the potential limitations of the instruction-supervised fine-tuning approach and improve its effectiveness in handling the challenges identified in the error analysis, the following strategies can be implemented: Enhanced Instruction Design: Refine the instruction design process to provide more detailed and informative prompts to the model. This can help address errors related to entity relation understanding and improve the model's comprehension of complex relations in the text. Multi-Task Learning: Implement a multi-task learning approach where the model is trained on multiple related tasks simultaneously. This can help the model learn to extract relations more accurately by leveraging information from different aspects of the text. Data Augmentation: Augment the training data with a diverse set of examples, including instances with multiple relations between entities and complex relation structures. This can help the model generalize better and improve its performance on challenging relation extraction tasks. Error Analysis Feedback Loop: Establish a feedback loop based on error analysis results to continuously refine the fine-tuning process. By identifying common error patterns and adjusting the training strategy accordingly, the model can progressively improve its performance over time. Regularization Techniques: Apply regularization techniques to prevent overfitting and enhance the model's generalization capabilities. Techniques such as dropout, weight decay, or early stopping can help mitigate errors caused by model complexity or data noise. By implementing these strategies, the instruction-supervised fine-tuning approach can be further improved to address the identified challenges and enhance the overall performance of the CRE-LLM framework.

Given the success of CRE-LLM in domain-specific Chinese relation extraction, how could this framework be adapted to tackle relation extraction tasks in other languages or domains?

To adapt the CRE-LLM framework for relation extraction tasks in other languages or domains, the following steps can be taken: Language-specific Fine-Tuning: Fine-tune the LLM on datasets in the target language to adapt the model's language understanding capabilities. This involves providing training data in the new language and adjusting the instruction-supervised fine-tuning process accordingly. Domain-specific Data Collection: Gather domain-specific datasets in the target domain to train the model on relevant relation extraction tasks. The training data should cover a wide range of relations and entities specific to the new domain. Prompt Customization: Customize the prompts used in the instruction-supervised fine-tuning process to align with the linguistic characteristics and relation structures of the new language or domain. This may involve translating prompts or designing new prompts tailored to the target context. Transfer Learning: Utilize transfer learning techniques to leverage the knowledge learned from the domain-specific Chinese relation extraction tasks. Transfer the fine-tuned model to the new language or domain and further fine-tune it on the specific relation extraction tasks in that context. Evaluation and Iteration: Evaluate the performance of the adapted CRE-LLM framework on relation extraction tasks in the new language or domain. Iterate on the fine-tuning process, prompt design, and model architecture based on the evaluation results to optimize performance. By following these steps and customizing the CRE-LLM framework for different languages and domains, it can be effectively adapted to tackle relation extraction tasks in diverse linguistic and contextual settings.