Work in progress: Making efficient use of language models for theorem proving

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• Albert Qiaochu Jiang (University of Cambridge)
• Tuesday 08 March 2022, 13:15-14:15
• Zoom.

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Language models are neural networks trained on broad data and adaptable to a wide range of downstream tasks. They have shown impressive capabilities in domains such as NLP , vision, robotics, and reasoning. For mathematical reasoning, several SOTA results have been achieved by language models, but the huge computational cost prevents them from a wider adoption in the formal mathematics community. We introduce two methods to make efficient use of language models for theorem proving – one based on augmenting the language model prompt with previous proof steps and the other based on sampling output candidates of larger quantity and diversity. Our model improved the state-of-the-art theorem proving success rate on the Archive of Formal Proofs from 33.2% to 39.6%, without incurring any further computational cost.

This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.

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