LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement

TL;DR

This paper presents a novel framework where large language models act as instructors to improve smaller models by analyzing errors and applying targeted training strategies, leading to significant performance gains.

Contribution

The paper introduces a new LLM-based instructor framework utilizing error analysis and contrastive learning to enhance smaller models' capabilities, outperforming existing methods.

Findings

Refined Llama-3-8b-Instruction surpasses ChatGPT in benchmarks.

Error-focused training improves mathematical reasoning and coding.

Contrastive learning yields balanced in-domain and out-of-domain performance.

Abstract

This paper introduces the innovative "LLMs-as-Instructors" framework, which leverages the advanced Large Language Models (LLMs) to autonomously enhance the training of smaller target models. Inspired by the theory of "Learning from Errors", this framework employs an instructor LLM to meticulously analyze the specific errors within a target model, facilitating targeted and efficient training cycles. Within this framework, we implement two strategies: "Learning from Error," which focuses solely on incorrect responses to tailor training data, and "Learning from Error by Contrast", which uses contrastive learning to analyze both correct and incorrect responses for a deeper understanding of errors. Our empirical studies, conducted with several open-source models, demonstrate significant improvements across multiple benchmarks, including mathematical reasoning, coding abilities, and factual knowledge. Notably, the refined Llama-3-8b-Instruction has outperformed ChatGPT, illustrating the effectiveness of our approach. By leveraging the strengths of both strategies, we have attained a more balanced performance improvement on both in-domain and out-of-domain benchmarks. Our code can be found at https://yingjiahao14.github.io/LLMs-as-Instructors-pages/.