Charles E Staats — accepted AI-conference papers

2024

Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization

Jin Peng Zhou, Charles E Staats, Wenda Li, Christian Szegedy, Kilian Q Weinberger, Yuhuai Wu

ICLR 2024poster

Large language models (LLM), such as Google's Minerva and OpenAI's GPT families, are becoming increasingly capable of solving mathematical quantitative reasoning problems. However, they still make unjustified logical and computational errors in their reasoning steps and answers. In this paper, we le…

Cited by 31 Source ↗PDF ↗code ↗Scholar ↗

2022

Autoformalization with Large Language Models

Yuhuai Wu, Albert Qiaochu Jiang, Wenda Li, Markus Norman Rabe, Charles E Staats, Mateja Jamnik +1

NeurIPS 2022accept

Autoformalization is the process of automatically translating from natural language mathematics to formal specifications and proofs. A successful autoformalization system could advance the fields of formal verification, program synthesis, and artificial intelligence. While the long-term goal of auto…

Cited by 189 Source ↗PDF ↗Scholar ↗