Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Abhishek Panigrahi
- sl:arxiv_num : 2302.06600
- sl:arxiv_published : 2023-02-13T18:55:52Z
- sl:arxiv_summary : Pre-trained language models can be fine-tuned to solve diverse NLP tasks,
including in few-shot settings. Thus fine-tuning allows the model to quickly
pick up task-specific ``skills,'' but there has been limited study of where
these newly-learnt skills reside inside the massive model. This paper
introduces the term skill localization for this problem and proposes a
solution. Given the downstream task and a model fine-tuned on that task, a
simple optimization is used to identify a very small subset of parameters
($\sim0.01$% of model parameters) responsible for ($>95$%) of the model's
performance, in the sense that grafting the fine-tuned values for just this
tiny subset onto the pre-trained model gives performance almost as well as the
fine-tuned model. While reminiscent of recent works on parameter-efficient
fine-tuning, the novel aspects here are that: (i) No further re-training is
needed on the subset (unlike, say, with lottery tickets). (ii) Notable
improvements are seen over vanilla fine-tuning with respect to calibration of
predictions in-distribution ($40$-$90$% error reduction) as well as the quality
of predictions out-of-distribution (OOD). In models trained on multiple tasks,
a stronger notion of skill localization is observed, where the sparse regions
corresponding to different tasks are almost disjoint, and their overlap (when
it happens) is a proxy for task similarity. Experiments suggest that
localization via grafting can assist certain forms of continual learning.@en
- sl:arxiv_title : Task-Specific Skill Localization in Fine-tuned Language Models@en
- sl:arxiv_updated : 2023-07-02T01:55:00Z
- sl:bookmarkOf : https://arxiv.org/abs/2302.06600
- sl:creationDate : 2023-08-25
- sl:creationTime : 2023-08-25T22:52:04Z
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