@inproceedings{lapokin-savchenko-2026-crl,
    title = "{CRL}-Prompt: Contrastive and Reinforcement Learning for Soft Prompt Tuning for Text Classification",
    author = "Lapokin, Danila  and
      Savchenko, Andrey",
    editor = "T.Y.S.S., Santosh  and
      Rodriguez, Juan Diego  and
      de Gibert, Ona",
    booktitle = "Proceedings of the 64th Annual Meeting 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.acl-srw.123/",
    pages = "1391--1398",
    ISBN = "979-8-89176-393-7",
    abstract = "Prompt choice is crucial in adapting language models to text classification tasks, particularly under low-resource conditions. Manual prompt engineering is time-consuming, non-scalable, and brittle, while current auto-prompting techniques are still far from maturity. This paper presents a two-stage method for prompt learning of frozen language models, CRL-Prompt, based on soft prompt initialization followed by contrastive and reinforcement-based refinement. An experimental study demonstrates that our approach achieves consistent improvements in accuracy over baseline prompt tuning strategies, with gains of up to 2.2{\%} while training fewer than 0.25{\%} of model parameters."
}