MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching

Liang Yue, Yihong Tang, Kehai Chen, Jie Liu, Min Zhang

Keywords: Instruction Fine-Tuning；Data Augmentation；Multi-Agent Systems；Natural Language Processing

TL;DR: We introduce MASTER, a multi-agent framework that enhances LLM instruction data through simulated pedagogical interactions, significantly improving reasoning and generalization.

Abstract: Instruction fine-tuning is crucial in NLP tasks, enhancing pretrained models' instruction-following capabilities and task-specific performance. However, obtaining high-quality fine-tuning data for large models is challenging due to data collection difficulties and high production costs. To address this, we propose MASTER, a novel data augmentation method that enriches original data through interactions among multiple agents with varying cognitive levels. We simulate three pedagogically grounded teaching scenarios, leveraging multi-agent conversations to generate high-quality teacher-student interaction data. Utilizing MASTER, we construct BOOST-QA, a fine-tuning dataset augmented from existing datasets like Orca-Math-200k, ProcQA, and OpenHermes2.5. Experiments show that models fine-tuned with BOOST-QA perform excellently across multiple benchmarks, demonstrating strong multitask generalization. Notably, MASTER significantly improves models' reasoning abilities in complex tasks, providing valuable insights for future research.

Primary Area: Deep learning (e.g., architectures, generative models, optimization for deep networks, foundation models, LLMs)

Submission Number: 28899