无需外部资源，通过任务插值生成多样指令数据提升大模型性能。
MDIT: A Model-free Data Interpolation Method for Diverse Instruction Tuning
- 用任务插值生成多样化指令数据，不依赖外部模型或资源。
- 在问答、数学推理和代码生成任务上显著提升模型表现。
- 适合需要高效自动数据增强的复杂场景应用。
随着大语言模型（LLMs）在各类任务中的广泛应用，指令微调已成为提升模型性能的关键方法。然而，现有数据管理策略在生成多样且全面的数据方面面临巨大挑战，限制了模型性能的进一步提升。为此，我们提出一种新型无模型数据插值方法MDIT，通过任务插值生成多样且高质量的指令数据，并引入基于多样性的聚类策略以确保训练数据的多样性。大量实验表明，使用MDIT微调的LLM在多个基准任务中表现出色，尤其在通用问答、数学推理和代码生成任务上均有显著提升。MDIT提供了一种高效、自动化的数据合成方法，在不依赖外部资源的前提下生成多样化指令数据，拓展了大模型在复杂环境中的应用潜力。
原文摘要 · Abstract (English)
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.
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