Papers by Yizhen Zhang

2 papers
TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts (2024.emnlp-main)

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Challenge: a framework for formal proof writing using formal languages like Lean4 is needed to prove mathematical theorems using formal language.
Approach: They propose a framework that trains a general-purpose LLM to be a Lean4 expert.
Outcome: The proposed framework achieves cumulative accuracies of 36.48% and 33.61% on MiniF2F-Valid and Test datasets.
Teaching Your Models to Understand Code via Focal Preference Alignment (2025.emnlp-main)

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Challenge: Existing methods for supervised fine-tuning focus on unit test feedback to construct preference pairs.
Approach: They propose a preference alignment framework that mimics human iterative debugging to refine Code LLMs.
Outcome: Experiments show that Preference Learning improves on BigCodeBench and BigCodeBind tasks.

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