MDIT：一种用于多样化指令微调的无模型数据插值方法
MDIT: A Model-free Data Interpolation Method for Diverse Instruction Tuning
AI总结：
针对指令微调中数据多样性不足的问题，提出无模型数据插值方法MDIT，通过任务插值和多样性聚类生成高质量指令数据，经其微调的LLMs在多基准任务中性能显著提升。
摘要：
随着大型语言模型（LLMs）在各类任务中的应用日益广泛，指令微调已成为提升模型性能的关键方法。然而，当前的数据管理策略在生成多样化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。为解决这一问题，我们提出MDIT——一种用于多样化指令微调的新型无模型数据插值方法，该方法通过执行任务插值生成多样化且高质量的指令数据。此外，它包含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，我们的方法在多个基准任务中取得了优异性能。经MDIT微调的LLMs在通用问答、数学推理和代码生成等众多任务中表现出显著提升。MDIT提供了一种高效且自动化的数据合成方法，无需依赖外部资源即可生成多样化的指令数据，同时拓展了LLMs在复杂环境中的应用潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
