1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CL2024
Distilling LLMs' Decomposition Abilities into Compact Language Models
Denis Tarasov, Kumar Shridhar
Large Language Models (LLMs) have demonstrated proficiency in their reasoning abilities, yet their large size presents scalability challenges and limits any further customization.…
cs.CL2023★ 1 cited
The ART of LLM Refinement: Ask, Refine, and Trust
Kumar Shridhar, Koustuv Sinha, Andrew Cohen +6
In recent years, Large Language Models (LLMs) have demonstrated remarkable generative abilities, but can they judge the quality of their own generations? A popular concept, referre…
cs.AI2023★ 1 cited
SCREWS: A Modular Framework for Reasoning with Revisions
Kumar Shridhar, Harsh Jhamtani, Hao Fang +3
Large language models (LLMs) can improve their accuracy on various tasks through iteratively refining and revising their output based on feedback. We observe that these revisions c…