281 citations · 352 across the 7 of their papers we have counts for
7 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,…
REFACTOR: Learning to Extract Theorems from Proofs
Jin Peng Zhou, Yuhuai Wu, Qiyang Li +1
Human mathematicians are often good at recognizing modular and reusable theorems that make complex mathematical results within reach. In this paper, we propose a novel method calle…
Lexinvariant Language Models
Qian Huang, Eric Zelikman, Sarah Li Chen +3
Token embeddings, a mapping from discrete lexical symbols to continuous vectors, are at the heart of any language model (LM). However, lexical symbol meanings can also be determine…
Language Model Cascades
David Dohan, Winnie Xu, Aitor Lewkowycz +9
Prompted models have demonstrated impressive few-shot learning abilities. Repeated interactions at test-time with a single model, or the composition of multiple models together, fu…
Exploring Length Generalization in Large Language Models
Cem Anil, Yuhuai Wu, Anders Andreassen +7
The ability to extrapolate from short problem instances to longer ones is an important form of out-of-distribution generalization in reasoning tasks, and is crucial when learning f…
Solving Quantitative Reasoning Problems with Language Models
Aitor Lewkowycz, Anders Andreassen, David Dohan +11
Language models have achieved remarkable performance on a wide range of tasks that require natural language understanding. Nevertheless, state-of-the-art models have generally stru…