通过多次迭代合并缓解任务干扰，提升多任务大模型性能。
Post-Hoc Merging is Not Enough: Many-Shot Model Merging with Loss-Gap Balancing
- 采用多轮迭代合并，动态调整各任务权重以减少信息丢失。
- 在最差任务上性能显著提升，有效缓解模型合并时的遗忘问题。
- 适合需要稳定多任务能力的场景，如通用语言模型构建。
模型合并已成为一种实用的训练后策略，通过整合多个任务专用模型，构建单一的多任务大型语言模型（LLM）。然而，现有方法大多依赖训练后的单次合并，容易引发任务间干扰，导致各任务信息被擦除。本文表明，将后处理合并替换为迭代式多轮合并协议可有效提升多任务性能。基于此，我们提出METIS：一种损失感知的多轮合并方法，通过任务级损失差距加权和基于共识的掩码机制，缓解后处理合并中的信息擦除问题。实验显示，METIS在表现最差的任务上取得显著性能提升，有效抑制了信息丢失。（项目页：https://imkyungjin.github.io/METIS/）
原文摘要 · Abstract (English)
Model merging has become a practical post-training strategy for building a single multi-task large language model (LLM) by combining multiple task-specialized models. However, most existing approaches rely on post-hoc merging, in which task-specific models are merged only once after training. This one-shot aggregation often suffers from task interference, leading to information erasure across individual tasks. In this work, we show that replacing post-hoc merging with an iterative many-shot merging protocol is effective in improving multi-task performance. Building on this insight, we propose METIS, Mitigating Erasure from Task Interference for Stable many-shot merging. METIS is a loss-aware many-shot merging method that addresses information erasure in post-hoc merging through task-wise loss-gap weighting and consensus-based masking. Notably, METIS exhibits significant performance improvement on the worst-performing task, effectively mitigating information erasure. (Project page: https://imkyungjin.github.io/METIS/)
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