Zixuan Yi

2 accepted papers

2026

Position: The Case for Theory-Level Autoformalization

Marcus Min, Deyuan Mike He, Zhaoyu Li, Zixuan Yi, Sharad Malik, Aarti Gupta +2

ICML 2026spotlight

Autoformalization, translating informal natural language into formal, machine-verifiable languages, has been framed as a tool to generate training data for neural theorem provers, with most work focusing on individual statements. This position paper argues for theory-level autoformalization: formali…

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2025

A Multi-modal Large Language Model with Graph-of-Thought for Effective Recommendation

Zixuan Yi, Iadh Ounis

NAACL 2025long

Chain-of-Thought (CoT) prompting has been shown to be effective in guiding Large Language Models (LLMs) to decompose complex tasks into multiple intermediate steps, and constructing a rational reasoning chain for inferring answers. However, the linear nature of CoT falls short from enabling LLMs to…

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