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Autoformalization in the Era of Large Language Models: A Survey

Created by
  • Haebom

Author

Ke Weng, Lun Du, Sirui Li, Wangyue Lu, Haozhe Sun, Hengyu Liu, Tiancheng Zhang

Outline

This paper provides a comprehensive overview of autoformalization, the process of converting informal mathematical propositions into verifiable formal representations. Driven by advances in artificial intelligence, and particularly large-scale language models (LLMs), the field of autoformalization has experienced rapid growth, bringing both new opportunities and unique challenges. In this paper, we review recent advances in autoformalization from both mathematical and LLM-centric perspectives, investigate applications of autoformalization across a range of mathematical domains and difficulty levels, and analyze the entire process from data preprocessing to model design and evaluation. We also explore the emerging role of autoformalization in increasing the verifiability of LLM-generated outputs, and highlight its potential for improving the reliability and inference capabilities of LLMs. Finally, we summarize the key open-source models and datasets that support current research, and discuss outstanding challenges and promising future directions in this field.

Takeaways, Limitations

Takeaways:
Advances in automatic formalization techniques using large-scale language models can greatly contribute to the automation and reliability of mathematical proofs.
Automatic formatting can play an important role in improving the inference ability and ensuring the reliability of LLM.
Automatic formalization research in various mathematical fields and levels of difficulty suggests new possibilities for formalizing and utilizing mathematical knowledge.
Sharing open source models and datasets plays a vital role in advancing research.
Limitations:
Although Limitations is not specifically mentioned in the paper, future challenges will likely include improving the accuracy and efficiency of automatic formalization techniques and enhancing their ability to process complex mathematical propositions.
There may be difficulties in developing generalized models for different mathematical representations and levels of difficulty.
The possibility of errors in automatic formatting due to limitations of LLM must be taken into account.
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