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rectifiable    
a. 可矫正的,可精馏的

可矫正的,可精馏的

rectifiable
adj 1: capable of being repaired or rectified; "reparable damage
to the car"; "rectifiable wrongs" [synonym: {reparable},
{rectifiable}] [ant: {irreparable}]

Rectifiable \Rec"ti*fi`a*ble\ (r?k"t?*f?`?*b'l), a.
1. Capable of being rectified; as, a rectifiable mistake.
[1913 Webster]

2. (Math.) Admitting, as a curve, of the construction of a
straight line equal in length to any definite portion of
the curve.
[1913 Webster]


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英文字典中文字典相关资料:


  • [2509. 22072] Fine-tuning Done Right in Model Editing - arXiv. org
    By clarifying a long-standing misconception and introducing a principled localized tuning strategy, we advance fine-tuning from an underestimated baseline to a leading method for model editing, establishing a solid foundation for future research
  • Fine-tuning Done Right in Model Editing | OpenReview
    TL;DR: This paper corrects prevailing misconceptions about the effectiveness of fine-tuning in model editing, and demonstrates that simple fine-tuning can effectively address model editing tasks
  • Paper page - Fine-tuning Done Right in Model Editing
    This paper revives fine-tuning for model editing, proving that by simply fixing its flawed implementation and applying a localized parameter update strategy, the simplest method becomes state-of-the-art, handling an unprecedented 100K sequential edits on models up to 72B parameters
  • Fine-tuning Done Right in Model Editing - Semantic Scholar
    By clarifying a long-standing misconception and introducing a principled localized tuning strategy, we advance fine-tuning from an underestimated baseline to a leading method for model editing, establishing a solid foundation for future research
  • [论文解读] Fine-tuning Done Right in Model Editing
    一句话总结 ¶ 揭示模型编辑中 fine-tuning 被低估的根因是错误的训练 pipeline（深度优先逐样本优化），修正为标准的广度优先 mini-batch 训练后，配合局部化参数调优形成 LocFT-BF，首次支持 10 万次连续编辑和 72B 模型规模。
  • Fine-tuning Done Right in Model Editing - GitHub
    揭示模型编辑中 fine-tuning 被低估的根因是错误的训练 pipeline（深度优先逐样本优化），修正为标准的广度优先 mini-batch 训练后，配合局部化参数调优形成 LocFT-BF，首次支持 10 万次连续编辑和 72B 模型规模。 领域现状：模型编辑旨在高效修改 LLM 中的特定事实知识而不重训练，主流方法包括参数扩展（GRACE）、元学习（MEND）和定位-编辑（ROME MEMIT）。 Fine-tuning 在该领域一直被视为弱 baseline，因"过拟合和灾难性遗忘"而被否定。





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