Fine-tuning Done Right in Model Editing
Fine-tuning, a foundational method for adapting large language models, has
long been considered ineffective for model editing. Here, we challenge this
belief, arguing that the reported failure arises not from the inherent
limitation of fine-tuning itself, but from adapting it to the sequential nature
of the editing task, a single-pass depth-first pipeline that optimizes each
sample to convergence before moving on. While intuitive, this depth-first
pipeline coupled with sample-wise updating over-optimizes each edit and induces
interference across edits. Our controlled experiments reveal that simply
restoring fine-tuning to the standard breadth-first (i.e., epoch-based)
pipeline with mini-batch optimization substantially improves its effectiveness
for model editing. Moreover, fine-tuning in editing also suffers from
suboptimal tuning parameter locations inherited from prior methods. Through
systematic analysis of tuning locations, we derive LocFT-BF, a simple and
effective localized editing method built on the restored fine-tuning framework.
Extensive experiments across diverse LLMs and datasets demonstrate that
LocFT-BF outperforms state-of-the-art methods by large margins. Notably, to our
knowledge, it is the first to sustain 100K edits and 72B-parameter models,10 x
beyond prior practice, without sacrificing general capabilities. 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.
68d9e8d90177a6054b013a2a
Wanli Yang
68d9e8d90177a6054b013a2b
Fei Sun
68d9e8d90177a6054b013a2c
Rui Tang
68d9e8d90177a6054b013a2d
Hongyu Zang
68d9e8d90177a6054b013a2e
Du Su
68d9e8d90177a6054b013a2f
Qi Cao
68d9e8d90177a6054b013a30
Jingang Wang
68d9e8d90177a6054b013a31
Huawei Shen
68d9e8d90177a6054b013a32
Xueqi Cheng
Fine-tuning Done Right in Model Editing
Fine-tuning, a foundational method for adapting large language models, has
long been considered ineffective for model editing. Here, we challenge this
belief, arguing that the reported failure arises not from the inherent
limitation of fine-tuning itself, but from adapting it to the sequential nature
of the editing task, a single-pass depth-first pipeline that optimizes each
sample to convergence before moving on. While intuitive, this depth-first
pipeline coupled with sample-wise updating over-optimizes each edit and induces
interference across edits. Our controlled experiments reveal that simply
restoring fine-tuning to the standard breadth-first (i.e., epoch-based)
pipeline with mini-batch optimization substantially improves its effectiveness
for model editing. Moreover, fine-tuning in editing also suffers from
suboptimal tuning parameter locations inherited from prior methods. Through
systematic analysis of tuning locations, we derive LocFT-BF, a simple and
effective localized editing method built on the restored fine-tuning framework.
Extensive experiments across diverse LLMs and datasets demonstrate that
LocFT-BF outperforms state-of-the-art methods by large margins. Notably, to our
knowledge, it is the first to sustain 100K edits and 72B-parameter models,10 x
beyond prior practice, without sacrificing general capabilities. 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.
Restoring fine-tuning to a breadth-first pipeline with mini-batch optimization and localized tuning parameters improves its effectiveness for model editing, outperforming state-of-the-art methods.
fine-tuning model editing depth-first pipeline breadth-first pipeline mini-batch optimization LocFT-BF localized editing LLMs parameter-efficient fine-tuning