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English(EN) LIFT: Scoped Constraints for Evaluating and Training Instruction Following

新的LIFT框架旨在提升语言模型的指令遵循能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出LIFT，一个用于评估和训练语言模型指令遵循能力的新框架。LIFT包含Lift-Bench，一个旨在通过使用作用域约束并区分主要任务和指令遵循目标来缓解奖励欺骗的基准。该框架还包含Lift-Train，一个比现有方法更不容易受到奖励欺骗的训练对应部分。初步实验表明，当前前沿模型在Lift-Bench更具挑战性的方面表现不佳，并且公开可用的基准和训练数据旨在提供对约束密度和可访问性的更精细控制。 AI

影响 引入了一个新的基准和训练方法，以提高语言模型的指令遵循能力并减少奖励欺骗。

排序理由 该集群描述了一篇介绍用于语言模型的新颖基准和训练框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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

新的LIFT框架旨在提升语言模型的指令遵循能力

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该集群描述了一篇介绍用于语言模型的新颖基准和训练框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Eran Goldstein, Yuval Belfer, Yonatan Belinkov, Daniel Gissin ·

    LIFT: Scoped Constraints for Evaluating and Training Instruction Following

    arXiv:2610.28819v1 Announce Type: new Abstract: Common instruction following (IF) benchmarks, such as IFEval, evaluate the ability of language models (LMs) to follow simple constraints that can easily be verified programmatically. These same constraints, in turn, are used as rewa…

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