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【第100期】SLM更懂LLM提示词
Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：Smaller Language Models Are Better Instruction EvolversSummary
This research paper investigates the surprising effectiveness of smaller language models (SLMs) in improving instruction data for larger language models (LLMs). The authors challenge the common assumption that larger models are always superior for this task, demonstrating through experiments across three scenarios that SLMs generate more complex and diverse instructions. They attribute this to SLMs having a broader output space, reducing overconfidence. Furthermore, the study proposes a new metric, Instruction Complex-Aware IFD (IC-IFD), for evaluating instruction effectiveness without requiring instruction tuning. The findings suggest SLMs offer a cost-effective and efficient alternative for enhancing LLM instruction data.
本研究论文探讨了小型语言模型（SLMs）在改进大型语言模型（LLMs）指令数据方面令人惊讶的高效性。作者挑战了大型模型在此任务上总是更优的常见假设，通过在三种场景中的实验表明，SLMs 能生成更复杂和多样化的指令。他们将此归因于 SLMs 具有更广泛的输出空间，从而减少了过度自信现象。此外，研究提出了一种新的评估指标——指令复杂感知 IFD（IC-IFD），用于在不需要指令微调的情况下评估指令的有效性。研究结果表明，SLMs 为提升 LLM 指令数据提供了一种具有成本效益且高效的替代方案。
原文链接：https://www.arxiv.org/abs/2412.11231
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By 任雨山Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：Smaller Language Models Are Better Instruction EvolversSummary
This research paper investigates the surprising effectiveness of smaller language models (SLMs) in improving instruction data for larger language models (LLMs). The authors challenge the common assumption that larger models are always superior for this task, demonstrating through experiments across three scenarios that SLMs generate more complex and diverse instructions. They attribute this to SLMs having a broader output space, reducing overconfidence. Furthermore, the study proposes a new metric, Instruction Complex-Aware IFD (IC-IFD), for evaluating instruction effectiveness without requiring instruction tuning. The findings suggest SLMs offer a cost-effective and efficient alternative for enhancing LLM instruction data.
本研究论文探讨了小型语言模型（SLMs）在改进大型语言模型（LLMs）指令数据方面令人惊讶的高效性。作者挑战了大型模型在此任务上总是更优的常见假设，通过在三种场景中的实验表明，SLMs 能生成更复杂和多样化的指令。他们将此归因于 SLMs 具有更广泛的输出空间，从而减少了过度自信现象。此外，研究提出了一种新的评估指标——指令复杂感知 IFD（IC-IFD），用于在不需要指令微调的情况下评估指令的有效性。研究结果表明，SLMs 为提升 LLM 指令数据提供了一种具有成本效益且高效的替代方案。
原文链接：https://www.arxiv.org/abs/2412.11231