用大模型零样本检测代码库中的数据集和软件实体。
Hidden Entity Detection from GitHub Leveraging Large Language Models
- 基于少样本提示学习，利用大模型识别代码库文本中的实体。
- 在无大量标注数据时仍能有效识别数据集与软件名称。
- 适合需要自动化知识抽取的开源项目分析场景。
从非结构化数据源构建知识库时，命名实体识别是一项关键任务。传统方法依赖大量训练数据，而大语言模型（LLM）通过预训练获得的能力，使零样本学习（ZSL）和少样本学习（FSL）成为可能，尤其适用于缺乏大规模标注数据的特定场景。本文研究了在GitHub代码仓库文本中，如何利用大语言模型自动检测数据集和软件实体。不同于以往仅关注命名实体的方法，本研究拓展了范围，涵盖由URL表示的仓库、在线资源等实体。通过探索多种少样本提示学习策略，提升模型对数据集与软件提及的识别能力。实验分析了大模型的有效性及学习策略，揭示了先进语言模型在自动化实体检测中的潜力。
原文摘要 · Abstract (English)
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.
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