On the Role of Semantic Proto-roles in Semantic Analysis: What do LLMs know about agency?

AbstractLarge language models (LLMs) are increasingly used in decision-making contexts, yet their ability to reason over event structure—an important component in the situational awareness needed to make complex decisions—is not well understood. By operationalizing proto-role theory, which characterizes agents via properties such as *instigation* and *volition* and patients via properties such as *change of state*, we examine the ability of LLMs to answer questions that require complex, multi-step event reasoning. Specifically, we investigate the extent to which LLMs capture semantic roles such as “agent” and “patient” through zero-shot prompts, and whether incorporating semantic proto-role labeling (SPRL) context improves semantic role labeling (SRL) performance in a zero-shot setting. We find that, while SPRL context sometimes degrades SRL accuracy in high-performing models (e.g., GPT-4o), it also uncovers an internal consistency between SPRL and SRL predictions that mirrors linguistic theory, and provides evidence that LLMs implicitly encode consistent multi-dimensional event role knowledge. Furthermore, our experiments support prior work showing that LLMs underperform human annotators in complex semantic analysis.