Authors: Debjit Paul, Mete Ismayilzada, Maxime Peyrard, Beatriz Borges, Antoine Bosselut, Robert West, Boi Faltings

Published on: April 04, 2023

Impact Score: 8.22

Arxiv code: Arxiv:2304.01904

Summary

• What is new: Introducing REFINER, a framework that enhances language models’ reasoning capabilities by utilizing a critic model for feedback on intermediate steps.
• Why this is important: Language models often make inappropriate deductions in their reasoning processes, leading to incorrect conclusions.
• What the research proposes: REFINER employs a critic model to provide structured feedback on language models’ intermediate reasoning steps, enabling iterative improvement.
• Results: Significant enhancements in reasoning tasks over baseline LMs and improved performance with GPT-3.5 and ChatGPT without additional finetuning.

Technical Details

Technological frameworks used: REFINER

Models used: GPT-3.5, ChatGPT

Data used: Three diverse reasoning tasks

Potential Impact

Education tech for personalized learning, AI development tools, and automated decision-making systems in various sectors might see significant impacts.

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