43 citations · 64 across the 4 of their papers we have counts for
4 papers
Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization
Jin Peng Zhou, Charles Staats, Wenda Li +3
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,…
Autoformalization with Large Language Models
Yuhuai Wu, Albert Q. Jiang, Wenda Li +4
Autoformalization is the process of automatically translating from natural language mathematics to formal specifications and proofs. A successful autoformalization system could adv…
When adversarial examples are excusable
Pieter-Jan Kindermans, Charles Staats
Neural networks work remarkably well in practice and theoretically they can be universal approximators. However, they still make mistakes and a specific type of them called adversa…
Self-attention Does Not Need Memory
Markus N. Rabe, Charles Staats
We present a very simple algorithm for attention that requires memory with respect to sequence length and an extension to self-attention that requires memory. Th…