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1
Introduction
2
What is a parameter
3
Intuition
4
Autoformalization
5
Model Translation
6
TwoShot Training
7
Failure Case
8
Takeaways
9
Translational Proof
10
Formal Sketch
11
Results
12
Benchmark
13
Examples
14
Alarm Proof
Description:
Explore the cutting-edge field of autoformalization with large language models in this 55-minute conference talk by Tony Wu from Google. Delve into the process of automatically translating natural language mathematics into formal specifications and proofs, and discover how this technology could revolutionize formal verification, program synthesis, and artificial intelligence. Examine the intuition behind autoformalization, learn about model translation and two-shot training techniques, and analyze failure cases and takeaways. Investigate translational proofs, formal sketches, and benchmark results through practical examples, including an alarm proof. Gain valuable insights into the future prospects of autoformalization and its potential impact on advancing mathematical research and artificial intelligence capabilities.

Autoformalization with Large Language Models - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM)
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