探索大型语言模型在主观跨度识别任务中的性能
Exploring the Performance of Large Language Models on Subjective Span Identification Tasks
AI总结：
本文探讨了大型语言模型在情感分析、攻击性语言识别和声明验证等任务中进行主观跨度识别的性能，评估了多种LLM策略的效果。
摘要：
识别相关文本跨度对于NLP中的多个下游任务至关重要，因为它有助于模型可解释性。尽管大多数跨度识别方法依赖于相对较小的预训练语言模型，如BERT，但一些最近的方法已经利用了最新一代的大语言模型（LLMs）来进行该任务。当前的研究主要集中在显式的跨度识别，如命名实体识别（NER），而使用LLMs在如基于方面的情感分析（ABSA）等任务中进行更主观的跨度识别则研究较少。在本文中，我们通过在三个流行的任务中评估各种LLMs在文本跨度识别中的性能来填补这一重要的空白，即情感分析、攻击性语言识别和声明验证。我们探索了多种LLM策略，如指令微调、上下文学习和思维链。我们的结果表明，文本中的潜在关系有助于LLM更精确地识别文本跨度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
