@inproceedings{guan-etal-2026-uerlens,
title = "{UERL}ens: Understanding Event Relations in Large Language Models",
author = "Guan, Yong and
Li, Zhiyuan and
Guo, Shaoru",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-short.38/",
pages = "463--471",
ISBN = "979-8-89176-391-3",
abstract = "Events exhibit rich semantic relations that are essential for understanding the unfolding of real-world processes. Although large language models (LLMs) have achieved strong performance on event relation extraction, how event relations are internally represented and utilized remains unclear. In this paper, we present UERLens, an interpretability framework for understanding event relations in LLMs. Specifically, we first construct UERBench, a counterfactual dataset for event relation analysis that covers causal, temporal, and sub-event relations. Based on counterfactual pairs, we identify relation-sensitive internal features by comparing model activations. We then examine the functional role of these features through model manipulation, including model intervention and model training. Experimental results show that event relations are encoded through structured and layer-specific internal features. Disabling relation-sensitive features leads to performance drops of over 22{\%}, while enhancing them yields improvements of up to 7{\%}. Furthermore, leveraging these interpretable features to train a lightweight classifier significantly improves event relation extraction, achieving F1 gains of up to 24{\%} for causal relations."
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<abstract>Events exhibit rich semantic relations that are essential for understanding the unfolding of real-world processes. Although large language models (LLMs) have achieved strong performance on event relation extraction, how event relations are internally represented and utilized remains unclear. In this paper, we present UERLens, an interpretability framework for understanding event relations in LLMs. Specifically, we first construct UERBench, a counterfactual dataset for event relation analysis that covers causal, temporal, and sub-event relations. Based on counterfactual pairs, we identify relation-sensitive internal features by comparing model activations. We then examine the functional role of these features through model manipulation, including model intervention and model training. Experimental results show that event relations are encoded through structured and layer-specific internal features. Disabling relation-sensitive features leads to performance drops of over 22%, while enhancing them yields improvements of up to 7%. Furthermore, leveraging these interpretable features to train a lightweight classifier significantly improves event relation extraction, achieving F1 gains of up to 24% for causal relations.</abstract>
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%0 Conference Proceedings
%T UERLens: Understanding Event Relations in Large Language Models
%A Guan, Yong
%A Li, Zhiyuan
%A Guo, Shaoru
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-391-3
%F guan-etal-2026-uerlens
%X Events exhibit rich semantic relations that are essential for understanding the unfolding of real-world processes. Although large language models (LLMs) have achieved strong performance on event relation extraction, how event relations are internally represented and utilized remains unclear. In this paper, we present UERLens, an interpretability framework for understanding event relations in LLMs. Specifically, we first construct UERBench, a counterfactual dataset for event relation analysis that covers causal, temporal, and sub-event relations. Based on counterfactual pairs, we identify relation-sensitive internal features by comparing model activations. We then examine the functional role of these features through model manipulation, including model intervention and model training. Experimental results show that event relations are encoded through structured and layer-specific internal features. Disabling relation-sensitive features leads to performance drops of over 22%, while enhancing them yields improvements of up to 7%. Furthermore, leveraging these interpretable features to train a lightweight classifier significantly improves event relation extraction, achieving F1 gains of up to 24% for causal relations.
%U https://aclanthology.org/2026.acl-short.38/
%P 463-471
Markdown (Informal)
[UERLens: Understanding Event Relations in Large Language Models](https://aclanthology.org/2026.acl-short.38/) (Guan et al., ACL 2026)
ACL
- Yong Guan, Zhiyuan Li, and Shaoru Guo. 2026. UERLens: Understanding Event Relations in Large Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 463–471, San Diego, California, United States. Association for Computational Linguistics.