@inproceedings{spaulding-etal-2025-role,
title = "On the Role of Semantic Proto-roles in Semantic Analysis: What do {LLM}s know about agency?",
author = "Spaulding, Elizabeth and
Ahmed, Shafiuddin Rehan and
Martin, James",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.623/",
doi = "10.18653/v1/2025.findings-acl.623",
pages = "12027--12048",
ISBN = "979-8-89176-256-5",
abstract = "Large 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."
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<abstract>Large 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.</abstract>
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%0 Conference Proceedings
%T On the Role of Semantic Proto-roles in Semantic Analysis: What do LLMs know about agency?
%A Spaulding, Elizabeth
%A Ahmed, Shafiuddin Rehan
%A Martin, James
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F spaulding-etal-2025-role
%X Large 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.
%R 10.18653/v1/2025.findings-acl.623
%U https://aclanthology.org/2025.findings-acl.623/
%U https://doi.org/10.18653/v1/2025.findings-acl.623
%P 12027-12048
Markdown (Informal)
[On the Role of Semantic Proto-roles in Semantic Analysis: What do LLMs know about agency?](https://aclanthology.org/2025.findings-acl.623/) (Spaulding et al., Findings 2025)
ACL