Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning
This addresses the data quality bottleneck in instruction tuning for LLMs, offering an automated solution that is incremental in improving existing methods.
The paper tackles the problem of expensive and time-consuming collection of high-quality instruction tuning data for large language models by proposing the Star-Agents framework, which automates data enhancement through multi-agent collaboration and assessment, resulting in optimized datasets that achieve an average 12% improvement and up to 40% gains in specific metrics like Fermi.
The efficacy of large language models (LLMs) on downstream tasks usually hinges on instruction tuning, which relies critically on the quality of training data. Unfortunately, collecting high-quality and diverse data is both expensive and time-consuming. To mitigate this issue, we propose a novel Star-Agents framework, which automates the enhancement of data quality across datasets through multi-agent collaboration and assessment. The framework adopts a three-pronged strategy. It initially generates diverse instruction data with multiple LLM agents through a bespoke sampling method. Subsequently, the generated data undergo a rigorous evaluation using a dual-model method that assesses both difficulty and quality. Finaly, the above process evolves in a dynamic refinement phase, where more effective LLMs are prioritized, enhancing the overall data quality. Our empirical studies, including instruction tuning experiments with models such as Pythia and LLaMA, demonstrate the effectiveness of the proposed framework. Optimized datasets have achieved substantial improvements, with an average increase of 12% and notable gains in specific metrics, such as a 40% improvement in Fermi, as evidenced by benchmarks like MT-bench, Vicuna bench, and WizardLM testset.