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PRISM 框架在训练期间增强 LLM 输出多样性

研究人员推出 PRISM,一个旨在增强大型语言模型 (LLM) 所生成输出多样性的新颖训练时框架。PRISM 通过将多样性直接整合到模型的训练过程中来解决模式崩溃问题,即 LLM 倾向于产生重复或过于熟悉的内容。该框架结合了提示-解决方案对的子模组化策展和随机嵌入调制,以鼓励生成不同的输出。在 WritingPrompts 和 BookMIA 等基准测试上的实验表明,PRISM 提高了生成质量和语义多样性,优于现有方法,并通过维持多样化的推广为强化学习提供了更 robust 的基础。 AI

影响 通过提高输出多样性和实现更好的强化学习来增强 LLM 的能力。

排序理由 详细介绍 LLM 训练新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

PRISM 框架在训练期间增强 LLM 输出多样性

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详细介绍 LLM 训练新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Qixin Xu, Zihan Zhu, Xiaojiang Liu, Bohan Lyu ·

    PRISM:通过隐式随机调制促进大型语言模型的采样多样性

    arXiv:2610.32101v1 Announce Type: new Abstract: Large language models (LLMs) exhibit mode collapse, where their output distribution concentrates on familiar semantic modes. When diversity disappears, repeated sampling becomes semantic repetition rather than discovery, making rare…