Papers by Shengzhong Zhang

1 papers
Retrieval-Augmented Language Models are Mimetic Theorem Provers (2025.findings-emnlp)

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Challenge: Large language models often fail to provide rigorous proof-based reasoning for research-level mathematics.
Approach: They propose a simple yet effective RAG framework that augments retrieved proofs with queries and document contexts to improve retrieval performance.
Outcome: The proposed framework improves retrieval performance by 34.19% . dual RAG can be used to prove research-level theorems in theoretical machine learning .

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