UERLens: Understanding Event Relations in Large Language Models

Challenge:	Existing studies on event relation extraction (ERE) have focused on improving model performance.
Approach:	They propose an interpretability framework for understanding event relations in large language models . they first construct a counterfactual dataset that includes causal, temporal, and sub-event relations .
Outcome:	The proposed framework improves event relation extraction by leveraging internal features to train a lightweight classifier.

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Overview

Challenge

Approach

Outcome

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Challenge:	Existing work on event relation extraction focuses on hierarchical, temporal and causal relations but ignores the interdependence between events.
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Language Models

Challenge:	Existing methods for ERE rely on large language models, but they face limitations.
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Event-Centric Natural Language Processing (2021.acl-tutorials)

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Muhao Chen, Hongming Zhang, Qiang Ning, Manling Li, Heng Ji, Kathleen McKeown, Dan Roth

Challenge:	This tutorial will provide an introduction to various methods for automating the extraction, conceptualization and prediction of events and their relations.
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Responsible & Trustworthy NLP

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Fine-Grained Temporal Relation Extraction (P19-1)

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Siddharth Vashishtha, Benjamin Van Durme, Aaron Steven White

Information Extraction & Text Mining

Challenge:	Existing methods for temporal relations and event durations are insufficient for determining the fine-grained temporal structure of complex events.
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Consistent Discourse-level Temporal Relation Extraction Using Large Language Models (2025.findings-emnlp)

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Yi Fan, Michael Strube

Language Models

Challenge:	Recent advances in large language models (LLMs) have spurred research on temporal relation extraction tasks.
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