Papers by Zhuochun Li

2 papers
Think Globally, Group Locally: Evaluating LLMs Using Multi-Lingual Word Grouping Games (2025.emnlp-main)

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Challenge: Large language models exhibit biases in reasoning abilities due to linguistic modality, even with similar content.
Approach: They propose a task inspired by the New York Times Connections: GlobalGroup that evaluates large language models in an abstract reasoning task across several languages.
Outcome: The proposed task evaluates models across multiple languages in English, Spanish, Chinese, Hindi, and Arabic.
Learning from Committee: Reasoning Distillation from a Mixture of Teachers with Peer-Review (2025.findings-acl)

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Challenge: Large language models (LLMs) have proven to be highly effective in addressing a wide range of complex tasks.
Approach: They propose a method that asks teachers to identify and explain student’s mistakes and then asks them to provide customized instruction learning data.
Outcome: The proposed method reduces the chance of teachers guessing incorrectly with flawed rationales, improving instructional data quality.

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