CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Models

arXiv:2404.01663v1 Announce Type: new
Abstract: Open large language models (LLMs) have significantly advanced the field of natural language processing, showcasing impressive performance across various tasks.Despite the significant advancements in LLMs, their effective operation still relies heavily on human input to accurately guide the dialogue flow, with agent tuning being a crucial optimization technique that involves human adjustments to the model for better response to such guidance.Addressing this dependency, our work introduces the TinyAgent model, trained on a meticulously curated high-quality …

abstract advanced agent arxiv collaboration cs.cl dialogue flow framework guide human language language models language processing large language large language models llms multi-agent natural natural language natural language processing performance processing small small language models tasks type