较小的语言模型更适合作为指导演进器

摘要

指导调整已被广泛应用以释放大型语言模型的全部潜力。值得注意的是，复杂和多样化的指导具有重要意义，因为它们可以有效地将模型与各种下游任务对齐。然而，目前构建大规模指导的方法主要偏爱强大的模型，如GPT-4或具有超过700亿参数的模型，基于这样的经验假设，即这些更大的语言模型（LLMs）固有地具有增强的能力。在这项研究中，我们质疑这一普遍假设，并深入探讨了较小语言模型（SLMs）在指导演变背景下的潜力。对指导演变的三种场景进行了广泛实验，结果显示，较小语言模型（SLMs）可以比LLMs合成更有效的指导。进一步分析表明，SLMs在指导演变过程中具有更广泛的输出空间，导致更复杂和多样化的变体。我们还观察到，现有的度量未能专注于指导的影响。因此，我们提出了指导复杂感知IFD（IC-IFD），它在原始IFD分数中引入指导复杂度，以更准确地评估指导数据的有效性。我们的源代码可在以下链接找到：https://github.com/HypherX/Evolution-Analysis {https://github.com/HypherX/Evolution-Analysis}

English

Instruction tuning has been widely used to unleash the complete potential of large language models. Notably, complex and diverse instructions are of significant importance as they can effectively align models with various downstream tasks. However, current approaches to constructing large-scale instructions predominantly favour powerful models such as GPT-4 or those with over 70 billion parameters, under the empirical presumption that such larger language models (LLMs) inherently possess enhanced capabilities. In this study, we question this prevalent assumption and conduct an in-depth exploration into the potential of smaller language models (SLMs) in the context of instruction evolution. Extensive experiments across three scenarios of instruction evolution reveal that smaller language models (SLMs) can synthesize more effective instructions than LLMs. Further analysis demonstrates that SLMs possess a broader output space during instruction evolution, resulting in more complex and diverse variants. We also observe that the existing metrics fail to focus on the impact of the instructions. Thus, we propose Instruction Complex-Aware IFD (IC-IFD), which introduces instruction complexity in the original IFD score to evaluate the effectiveness of instruction data more accurately. Our source code is available at: https://github.com/HypherX/Evolution-Analysis{https://github.com/HypherX/Evolution-Analysis}

较小的语言模型更适合作为指导演进器

Smaller Language Models Are Better Instruction Evolvers

摘要

Summary

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