MDIT: A Model-free Data Interpolation Method for Diverse Instruction Tuning
This work addresses a critical bottleneck in data management for instruction tuning, enabling more efficient and automatic generation of diverse training data without external resources, which is incremental but impactful for enhancing LLM applications in complex environments.
The paper tackles the challenge of generating diverse and comprehensive data for instruction tuning in Large Language Models by proposing MDIT, a model-free data interpolation method that uses task interpolation and clustering strategies, resulting in significant performance improvements in tasks like general question answering, math reasoning, and code generation.
As Large Language Models (LLMs) are increasingly applied across various tasks, instruction tuning has emerged as a critical method for enhancing model performance. However, current data management strategies face substantial challenges in generating diverse and comprehensive data, restricting further improvements in model performance. To address this gap, we propose MDIT, a novel model-free data interpolation method for diverse instruction tuning, which generates varied and high-quality instruction data by performing task interpolation. Moreover, it contains diversity-based clustering strategies to ensure the diversity of the training data. Extensive experiments show that our method achieves superior performance in multiple benchmark tasks. The LLMs finetuned with MDIT show significant improvements in numerous tasks such as general question answering, math reasoning, and code generation. MDIT offers an efficient and automatic data synthetic method, generating diverse instruction data without depending on external resources while expanding the application potential of LLMs in complex environments.