评测大模型在主观文本片段识别任务中的表现，发现上下文关联有助于精准定位。
Exploring the Performance of Large Language Models on Subjective Span Identification Tasks
- 对比指令微调、上下文学习等策略，评估大模型对文本片段的识别能力
- 在情感分析、攻击性语言识别和论断验证任务中，大模型表现优于传统方法
- 适合关注大模型可解释性与主观判断能力的研究者参考
在自然语言处理中，识别相关文本片段对提升模型可解释性至关重要。尽管多数片段识别方法依赖于BERT等较小预训练模型，近年来已有少数研究尝试利用最新一代大语言模型（LLMs）完成该任务。当前工作主要集中于命名实体识别等明确的片段识别，而基于大模型的主观片段识别（如基于方面的情感分析）仍缺乏系统探索。本文填补这一空白，评估多种大模型在三种典型任务——情感分析、攻击性语言识别与论断验证——中的文本片段识别表现。我们测试了指令微调、上下文学习及思维链等多种策略。结果表明，文本内部的语义关联有助于大模型更准确地定位关键片段。
原文摘要 · Abstract (English)
Identifying relevant text spans is important for several downstream tasks in NLP, as it contributes to model explainability. While most span identification approaches rely on relatively smaller pre-trained language models like BERT, a few recent approaches have leveraged the latest generation of Large Language Models (LLMs) for the task. Current work has focused on explicit span identification like Named Entity Recognition (NER), while more subjective span identification with LLMs in tasks like Aspect-based Sentiment Analysis (ABSA) has been underexplored. In this paper, we fill this important gap by presenting an evaluation of the performance of various LLMs on text span identification in three popular tasks, namely sentiment analysis, offensive language identification, and claim verification. We explore several LLM strategies like instruction tuning, in-context learning, and chain of thought. Our results indicate underlying relationships within text aid LLMs in identifying precise text spans.
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