Summary of Herald: a Natural Language Annotated Lean 4 Dataset, by Guoxiong Gao et al.
Herald: A Natural Language Annotated Lean 4 Dataset
by Guoxiong Gao, Yutong Wang, Jiedong Jiang, Qi Gao, Zihan Qin, Tianyi Xu, Bin Dong
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel framework for translating the Mathlib4 corpus into natural language using large language models (LLMs) and dual augmentation strategies. The authors employ the Lean-jixia system, a Lean 4 analyzer, to leverage its tactic-based and informal-based approaches. They present the results of this pipeline on Mathlib4 as Herald (Hierarchy and Retrieval-based Translated Lean Dataset). Additionally, they propose the Herald Translator, which is fine-tuned on Herald. The model achieves high accuracy in formalizing statements in various datasets, outperforming existing models. Furthermore, the authors demonstrate a section-level translation framework for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to understand complex math problems by using computers to translate them into natural language. It’s hard to find datasets that can help train these computer programs because they require both math and language skills. To solve this problem, the researchers created a new way to translate math problems from one format to another. They also developed a special system called Lean-jixia that helps with this process. The results show that their method is very accurate and can even be used for real-world applications like translating complex math texts. |
Keywords
» Artificial intelligence » Translation
