LLMs-as-instructors : Learning from errors toward automating model improvement

Publication Type

Conference Proceeding Article

Publication Date

11-2024

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.

Keywords

Large Language Models, LLM, Learning from errors, Model training

Discipline

Artificial Intelligence and Robotics

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Publication

Empirical Methods in Natural Language Processing EMNLP Findings

Identifier

10.48550/arXiv.2407.00497

Publisher

Empirical Methods in Natural Language Processing Conference

City or Country

Miami

Additional URL

https://doi.org/10.48550/arXiv.2407.00497

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