用大模型实现生物文献多标签句子分类，支持长文本与复杂任务。
Multi-label Sequential Sentence Classification via Large Language Model
- 基于大模型设计提示词，结合示例和目标描述提升理解。
- 提出自加权对比损失，有效支持多标签分类任务。
- 构建新数据集biorc800，适用于生物医学领域信息提取。
科学文献中的序列句子分类（SSC）对细粒度信息检索和摘要生成等下游任务至关重要。然而，现有方法受限于模型规模、序列长度及单标签设定。本文提出LLM-SSC框架，利用大语言模型（LLM）在单标签与多标签场景下进行句子分类。不同于以往使用小中型模型的方法，该框架通过设计的提示词生成标签，借助示范和查询描述增强任务理解。我们还提出一种具有自动加权机制的多标签对比学习损失函数，以支持多标签分类。为支持多标签分析，本文发布新数据集biorc800，主要包含生物医学领域未结构化摘要及其人工标注。实验表明，LLM-SSC在上下文学习与任务微调设置下均表现优异。代码与数据集已公开于：https://github.com/ScienceNLP-Lab/LLM-SSC。
原文摘要 · Abstract (English)
Sequential sentence classification (SSC) in scientific publications is crucial for supporting downstream tasks such as fine-grained information retrieval and extractive summarization. However, current SSC methods are constrained by model size, sequence length, and single-label setting. To address these limitations, this paper proposes LLM-SSC, a large language model (LLM)-based framework for both single- and multi-label SSC tasks. Unlike previous approaches that employ small- or medium-sized language models, the proposed framework utilizes LLMs to generate SSC labels through designed prompts, which enhance task understanding by incorporating demonstrations and a query to describe the prediction target. We also present a multi-label contrastive learning loss with auto-weighting scheme, enabling the multi-label classification task. To support our multi-label SSC analysis, we introduce and release a new dataset, biorc800, which mainly contains unstructured abstracts in the biomedical domain with manual annotations. Experiments demonstrate LLM-SSC's strong performance in SSC under both in-context learning and task-specific tuning settings. We release biorc800 and our code at: https://github.com/ScienceNLP-Lab/LLM-SSC.
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