Potential And Limitations Of Llms In Capturing Structured Semantics: A Case Study On SRL

Cheng Ning, Yan Zhaohui, Wang Ziming, Li Zhijie, Yu Jiaming, Zheng Zilong, Tu Kewei, Xu Jinan, Han Wenjuan. Arxiv 2024

[Paper]    
Ethics And Bias Few Shot Interpretability And Explainability Prompting Reinforcement Learning

Large Language Models (LLMs) play a crucial role in capturing structured semantics to enhance language understanding, improve interpretability, and reduce bias. Nevertheless, an ongoing controversy exists over the extent to which LLMs can grasp structured semantics. To assess this, we propose using Semantic Role Labeling (SRL) as a fundamental task to explore LLMs’ ability to extract structured semantics. In our assessment, we employ the prompting approach, which leads to the creation of our few-shot SRL parser, called PromptSRL. PromptSRL enables LLMs to map natural languages to explicit semantic structures, which provides an interpretable window into the properties of LLMs. We find interesting potential: LLMs can indeed capture semantic structures, and scaling-up doesn’t always mirror potential. Additionally, limitations of LLMs are observed in C-arguments, etc. Lastly, we are surprised to discover that significant overlap in the errors is made by both LLMs and untrained humans, accounting for almost 30% of all errors.

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