MDIT: A Model-free Data Interpolation Method for Diverse Instruction Tuning

2025-04-09

AI Summary

To address the limitations of instruction tuning—namely, insufficient training data diversity, high manual construction costs, and reliance on external models—this paper proposes a fully zero-resource, model-agnostic instruction data synthesis method. The approach comprises three core components: (1) a novel task-level instruction interpolation mechanism that generates new instructions via linear combination of task vectors; (2) diversity-aware k-means clustering to ensure broad coverage of the multi-task semantic space; and (3) template composition coupled with semantic consistency filtering to guarantee output quality. Crucially, the method requires no pretrained models, human annotations, or additional computational resources. Evaluated across diverse benchmarks—including general question answering, mathematical reasoning, and code generation—it consistently improves LLM performance, demonstrating strong generalization and plug-and-play applicability.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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.

Problem

• Generating diverse and comprehensive instruction tuning data
• Improving model performance without external resources
• Enhancing LLMs in complex tasks automatically

Innovation

• Model-free data interpolation for diverse instruction tuning
• Task interpolation generates varied high-quality instruction data
• Diversity-based clustering ensures training data diversity