← Search

Chenrui Mao

2 accepted papers

2025

How do LLMs’ Preferences Affect Event Argument Extraction? CAT: Addressing Preference Traps in Unsupervised EAE

ACL 2025finding

Large Language Models (LLMs) have significantly improved the performance of unsupervised Event Argument Extraction (EAE) tasks. However, LLMs’ inherent preferences severely hinder their effectiveness in EAE, leading to what we term preference traps, namely, the Prior Knowledge Trap, the Sycophancy H…

2024

Scented-EAE: Stage-Customized Entity Type Embedding for Event Argument Extraction

ACL 2024findings

Existing methods for incorporating entities into EAE rely on prompts or NER. They typically fail to explicitly explore the role of entity types, which results in shallow argument comprehension and often encounter three issues: (1) weak semantic associations due to missing role-entity correspondence…