用检索增强与自我修正，让大模型在语义角色标注上超越传统模型。
LLMs Can Also Do Well! Breaking Barriers in Semantic Role Labeling via Large Language Models
- 引入外部语言知识和自我纠错机制提升大模型表现
- 在中英文三个基准上达到最优性能，首次超越编码器-解码器模型
- 适合关注大模型在深层语义任务应用的研究者
语义角色标注（SRL）是自然语言处理中的关键任务。尽管生成式解码器类大语言模型（LLMs）在多种NLP任务中表现卓越，但在SRL上仍落后于主流编码器-解码器模型（如BERT类）。本文通过引入两种机制：（a）检索增强生成，使LLM能利用外部语言知识（如谓词与论元结构描述）；（b）自我纠正，使模型可识别并修正不一致的标注输出。我们在三个广泛使用的SRL基准（CPB1.0、CoNLL-2009、CoNLL-2012）上进行大量实验，结果表明，该方法在中英文任务中均达到当前最优性能，首次实现大语言模型在SRL上超越编码器-解码器范式，具有重要突破意义。
原文摘要 · Abstract (English)
Semantic role labeling (SRL) is a crucial task of natural language processing (NLP). Although generative decoder-based large language models (LLMs) have achieved remarkable success across various NLP tasks, they still lag behind state-of-the-art encoder-decoder (BERT-like) models in SRL. In this work, we seek to bridge this gap by equipping LLMs for SRL with two mechanisms: (a) retrieval-augmented generation and (b) self-correction. The first mechanism enables LLMs to leverage external linguistic knowledge such as predicate and argument structure descriptions, while the second allows LLMs to identify and correct inconsistent SRL outputs. We conduct extensive experiments on three widely-used benchmarks of SRL (CPB1.0, CoNLL-2009, and CoNLL-2012). Results demonstrate that our method achieves state-of-the-art performance in both Chinese and English, marking the first successful application of LLMs to surpass encoder-decoder approaches in SRL.
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