@inproceedings{wang-etal-2026-pibe,
    title = "{P}ib{E}-{MPP}: A Play-it-by-Ear Masking Performance Plug-in for {LLM}s",
    author = "Wang, Mengwei  and
      Niu, Simin  and
      Liang, Xun  and
      Ma, Yuefeng  and
      Zhang, Sensen  and
      Yang, Jiawei  and
      Song, Shichao  and
      Wang, Hanyu  and
      Lai, Huayi",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.105/",
    pages = "2214--2225",
    ISBN = "979-8-89176-395-1",
    abstract = "Treating random masking as a performance plug-in for large language models (LLMs) offers three advantages: low coupling to the task, the model, and training resources. However, the critical drawback is that its gains are highly stochastic. Motivated by this, we propose play-it-by-ear masking performance plug-in (PibE-MPP), which enables LLMs to adaptively select masking target combinations for each task, retaining these advantages and mitigating the drawback. Specifically, we pose two core questions{---}what are the masking targets and what is the masking strategy under 7 constraints obtained from these advantages and a drawback. For the first question, we select all attention heads in the last layer as masking targets by constructing a first-order Markov process with alternating hidden state and information fusion. The feasibility of this target is validated by random masking experiments. For the second question, we first construct a small yet interpretable candidate set by proposing a three-axis mapping and a mean-based criterion for fusion features of masking targets. We then propose an axis-variance minimization to select a compact masking-target combination, reducing sensitivity to outlier targets. Experiments on 6 LLMs (Qwen and LLaMA) and 24 datasets demonstrate PibE-MPP{'}s effectiveness and generality, gain stability, and domain performance, and verify the necessity of its final module, providing empirical evidence of its transferability across tasks and models. The code is available at https://github.com/wtctcop/PibE-MPP."
}