An Evaluation Benchmark for Autoformalization in Lean4
June 01, 2024 ยท Declared Dead ยท ๐ Tiny Papers @ ICLR
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Authors
Aryan Gulati, Devanshu Ladsaria, Shubhra Mishra, Jasdeep Sidhu, Brando Miranda
arXiv ID
2406.06555
Category
cs.LG: Machine Learning
Cross-listed
cs.AI,
cs.CL,
cs.PL
Citations
3
Venue
Tiny Papers @ ICLR
Last Checked
5 months ago
Abstract
Large Language Models (LLMs) hold the potential to revolutionize autoformalization. The introduction of Lean4, a mathematical programming language, presents an unprecedented opportunity to rigorously assess the autoformalization capabilities of LLMs. This paper introduces a novel evaluation benchmark designed for Lean4, applying it to test the abilities of state-of-the-art LLMs, including GPT-3.5, GPT-4, and Gemini Pro. Our comprehensive analysis reveals that, despite recent advancements, these LLMs still exhibit limitations in autoformalization, particularly in more complex areas of mathematics. These findings underscore the need for further development in LLMs to fully harness their potential in scientific research and development. This study not only benchmarks current LLM capabilities but also sets the stage for future enhancements in autoformalization.
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