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20242026
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cs.CL2026

Mitigating Gender Bias via Fostering Exploratory Thinking in LLMs

Kangda Wei, Hasnat Md Abdullah, Ruihong Huang

Large Language Models (LLMs) often exhibit gender bias, resulting in unequal treatment of male and female subjects across different contexts. To address this issue, we propose a no…

cs.CL2025

CliME: Evaluating Multimodal Climate Discourse on Social Media and the Climate Alignment Quotient (CAQ)

Abhilekh Borah, Hasnat Md Abdullah, Kangda Wei +1

The rise of Large Language Models (LLMs) has raised questions about their ability to understand climate-related contexts. Though climate change dominates social media, analyzing it…

cs.CL2025

LegalCore: A Dataset for Event Coreference Resolution in Legal Documents

Kangda Wei, Xi Shi, Jonathan Tong +5

Recognizing events and their coreferential mentions in a document is essential for understanding semantic meanings of text. The existing research on event coreference resolution is…

cs.CL2025

Are LLMs Good Annotators for Discourse-level Event Relation Extraction?

Kangda Wei, Aayush Gautam, Ruihong Huang

Large Language Models (LLMs) have demonstrated proficiency in a wide array of natural language processing tasks. However, its effectiveness over discourse-level event relation extr…

cs.CL2024

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing

Jiangshu Du, Yibo Wang, Wenting Zhao +37

This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and ques…