重新思考指令质量：LIFT 即所需
Rethinking the Instruction Quality: LIFT is What You Need
AI总结：
针对指令微调中数据质量提升的冗余与局限问题，提出 LIFT 范式，通过拓宽数据分布并消除冗余来提升指令质量，实验显示其能超越最先进结果。
摘要：
指令微调是一种通过指令数据集增强大型语言模型（LLM）性能的专门技术，其效果在很大程度上依赖于所用数据的质量。现有的质量改进方法通过数据集扩充或筛选来改变指令数据。然而，扩充方法存在数据冗余的风险，可能损害 LLM 的性能，而筛选方法则将 LLM 的潜力限制在原始数据集内。我们的目标是超越原始数据质量，同时避免这些缺陷。为此，我们提出了 LIFT（LLM 指令融合迁移），一种新颖且多用途的范式，旨在将指令质量提升至新高度。LIFT 策略性地拓宽数据分布以涵盖更多高质量子空间，并消除冗余，聚焦于整体数据子空间中的高质量片段。实验结果表明，即使仅使用我们范式筛选出的有限数量的高质量指令数据，LLM 不仅能在各种任务中持续保持稳健性能，还能超越一些最先进的结果，凸显了我们的范式在指令质量提升方面的显著成效。
英文摘要：
Instruction tuning, a specialized technique to enhance large language model (LLM) performance via instruction datasets, relies heavily on the quality of employed data. Existing quality improvement methods alter instruction data through dataset expansion or curation. However, the expansion method risks data redundancy, potentially compromising LLM performance, while the curation approach confines the LLM's potential to the original dataset. Our aim is to surpass the original data quality without encountering these shortcomings. To achieve this, we propose LIFT (LLM Instruction Fusion Transfer), a novel and versatile paradigm designed to elevate the instruction quality to new heights. LIFT strategically broadens data distribution to encompass more high-quality subspaces and eliminates redundancy, concentrating on high-quality segments across overall data subspaces. Experimental results demonstrate that, even with a limited quantity of high-quality instruction data selected by our paradigm, LLMs not only consistently uphold robust performance across various tasks but also surpass some state-of-the-art results, highlighting the significant improvement in instruction quality achieved by our paradigm.
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。
