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.

语言模型在自然语言任务中表现出令人印象深刻的性能，然而，在自然语言推理方面，语言模型仍面临幻觉、生成不正确的中间推理步骤和数学错误等挑战。最近的研究集中于通过自我改进和反馈来增强语言模型。然而，现有的方法依赖于单一的通用反馈来源，无法解决语言模型生成推理链中出现的多种错误类型。在这项工作中，我们提出了一种多方面反馈的迭代改进框架，该框架整合了多个反馈模块，包括冻结的语言模型和外部工具，每个模块都专注于特定的错误类别。我们的实验结果证明了我们的方法在解决语言模型生成的推理链中的几个错误，并因此提高了语言模型在多个推理任务中的整体性能。在数学推理中，我们看到了相对改进高达 20%，在逻辑推断中高达 18%。

MAF: 提升大型语言模型的推理能力的多方面反馈

MAF: Multi-Aspect Feedback for Improving Reasoning in Large Language  Models

The emergence of ChatGPT has been rapid, and although it has demonstrated
positive impacts in certain domains, its influence is not universally
advantageous. Our analysis focuses on ChatGPT's capabilities in Mathematics
Education, particularly in teaching basic Linear Algebra. While there are
instances where ChatGPT delivers accurate and well-motivated answers, it is
crucial to recognize numerous cases where it makes significant mathematical
errors and fails in logical inference. These occurrences raise concerns
regarding the system's genuine understanding of mathematics, as it appears to
rely more on visual patterns rather than true comprehension. Additionally, the
suitability of ChatGPT as a teacher for students also warrants consideration.

本文探讨了 ChatGPT 在数学教育中的能力，尤其是在基本线性代数的教学方面。虽然有时 ChatGPT 可以提供准确且有动机的答案，但显然它更多地依赖于视觉模式而非真正的理解，而且作为学生的老师是否合适也值得考虑。