3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.AI2024★ 1 cited
Orchestrating LLMs with Different Personalizations
Jin Peng Zhou, Katie Z Luo, Jingwen Gu +3
This paper presents a novel approach to aligning large language models (LLMs) with individual human preferences, sometimes referred to as Reinforcement Learning from \textit{Person…
cs.AI2024★ 3 cited
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,…
cs.AI2024
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…