English(EN) ORBIT: Preserving Foundational Language Capabilities in GenRetrieval via Origin-Regulated Merging

新的ORBIT方法可防止LLM在微调过程中发生灾难性遗忘

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-12 17:14

研究人员推出了一种名为ORBIT的新方法，旨在防止大型语言模型在特定任务微调过程中丢失其基础语言能力。这种被称为灾难性遗忘的问题在生成检索任务中尤为普遍，并且与模型参数的分歧有关。ORBIT通过监控微调模型和原始模型权重之间的距离来解决这个问题，当超过设定的阈值时，采用权重平均策略来限制参数漂移。实验表明，ORBIT能有效保留文本和检索性能，优于现有的持续学习和正则化技术。 AI

影响在特定任务的LLM微调过程中保留通用语言能力，可能提高模型的通用性。

排序理由发表了一篇介绍LLM微调新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

LLM
ORBIT

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arXiv cs.CL TIER_1 English(EN) · Xinyang Yi · 2026-05-12 17:14

ORBIT：在源约束合并的GenRetrieval中保留基础语言能力

Despite the rapid advancements in large language model (LLM) development, fine-tuning them for specific tasks often results in the catastrophic forgetting of their general, language-based reasoning abilities. This work investigates and addresses this challenge in the context of t…

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ORBIT：在源约束合并的GenRetrieval中保留基础语言能力

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