MAF: Multi-Aspect Feedback for Improving Reasoning in Large Language
  Models
Deepak Nathani David Wang Liangming Pan William Yang Wang
Language Models (LMs) have shown impressive performance in various natural
language tasks. However, when it comes to natural language reasoning, LMs still
face challenges such as hallucination, generating incorrect intermediate
reasoning steps, and making mathematical errors. Recent research has focused on
enhancing LMs through self-improvement using feedback. Nevertheless, existing
approaches relying on a single generic feedback source fail to address the
diverse error types found in LM-generated reasoning chains. In this work, we
propose Multi-Aspect Feedback, an iterative refinement framework that
integrates multiple feedback modules, including frozen LMs and external tools,
each focusing on a specific error category. Our experimental results
demonstrate the efficacy of our approach to addressing several errors in the
LM-generated reasoning chain and thus improving the overall performance of an
LM in several reasoning tasks. We see a relative improvement of up to 20% in
Mathematical Reasoning and up to 18% in Logical Entailment.