Hidden Entity Detection from GitHub Leveraging Large Language Models
This work addresses the need for automated knowledge base construction from unstructured data in specialized domains like software development, but it is incremental as it builds on existing LLM-based entity detection methods.
The paper tackled the problem of detecting datasets and software mentions in GitHub repository texts using Large Language Models (LLMs) with few-shot learning, achieving automated entity detection in specialized scenarios where large-scale training data is unavailable.
Named entity recognition is an important task when constructing knowledge bases from unstructured data sources. Whereas entity detection methods mostly rely on extensive training data, Large Language Models (LLMs) have paved the way towards approaches that rely on zero-shot learning (ZSL) or few-shot learning (FSL) by taking advantage of the capabilities LLMs acquired during pretraining. Specifically, in very specialized scenarios where large-scale training data is not available, ZSL / FSL opens new opportunities. This paper follows this recent trend and investigates the potential of leveraging Large Language Models (LLMs) in such scenarios to automatically detect datasets and software within textual content from GitHub repositories. While existing methods focused solely on named entities, this study aims to broaden the scope by incorporating resources such as repositories and online hubs where entities are also represented by URLs. The study explores different FSL prompt learning approaches to enhance the LLMs' ability to identify dataset and software mentions within repository texts. Through analyses of LLM effectiveness and learning strategies, this paper offers insights into the potential of advanced language models for automated entity detection.