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Computer Science > Machine Learning

arXiv:2411.00873 (cs)
[Submitted on 31 Oct 2024]

Title:CleaR: Towards Robust and Generalized Parameter-Efficient
Fine-Tuning for Noisy Label Learning

Authors:Yeachan Kim, Junho Kim, SangKeun Lee
View a PDF of the paper titled CleaR: Towards Robust and Generalized
Parameter-Efficient Fine-Tuning for Noisy Label Learning, by Yeachan
Kim and 2 other authors
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    Abstract:Parameter-efficient fine-tuning (PEFT) has enabled the
    efficient optimization of cumbersome language models in
    real-world settings. However, as datasets in such environments
    often contain noisy labels that adversely affect performance,
    PEFT methods are inevitably exposed to noisy labels. Despite this
    challenge, the adaptability of PEFT to noisy environments remains
    underexplored. To bridge this gap, we investigate various PEFT
    methods under noisy labels. Interestingly, our findings reveal
    that PEFT has difficulty in memorizing noisy labels due to its
    inherently limited capacity, resulting in robustness. However, we
    also find that such limited capacity simultaneously makes PEFT
    more vulnerable to interference of noisy labels, impeding the
    learning of clean samples. To address this issue, we propose
    Clean Routing (CleaR), a novel routing-based PEFT approach that
    adaptively activates PEFT modules. In CleaR, PEFT modules are
    preferentially exposed to clean data while bypassing the noisy
    ones, thereby minimizing the noisy influence. To verify the
    efficacy of CleaR, we perform extensive experiments on diverse
    configurations of noisy labels. The results convincingly
    demonstrate that CleaR leads to substantially improved
    performance in noisy environments.

Comments: Published at ACL 2024 Main Conference
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as:  arXiv:2411.00873 [cs.LG]
          (or arXiv:2411.00873v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2411.00873
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Junho Kim [view email]
[v1] Thu, 31 Oct 2024 05:11:58 UTC (1,334 KB)
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    Generalized Parameter-Efficient Fine-Tuning for Noisy Label
    Learning, by Yeachan Kim and 2 other authors
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