@inproceedings{debnath-etal-2026-cuet,
    title = "Cuet Yet Another Baseline@{D}ravidian{L}ang{T}ech 2026: Shared Task on Prompt Recovery for {LLM} in {T}elugu",
    author = "Debnath, Rotna Dipika  and
      Ruhi, Shahrin Afroz Hoque  and
      Labiba, Ayesha  and
      Mallik, Arpita  and
      Murad, Hasan",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Rajiakodi, Saranya  and
      Navaneethakrishnan, Subalalitha  and
      Chinnappa, Dhivya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Shanmugavadivel, Kogilavani  and
      Rajalakshmi, Ratnavel",
    booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
    month = jul,
    year = "2026",
    address = "Underline (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.dravidianlangtech-1.25/",
    pages = "191--195",
    ISBN = "979-8-89176-401-9",
    abstract = "Prompt recovery in large language models (LLMs) is the task of inferring the communicative intent and stylistic framing of the original instruction from model-generated output. This task is especially challenging for low-resource Dravidian languages such as Telugu, where agglutinative morphology, register variation, and scarce annotated data complicate stylistic modelling. In this paper, we present our system for the Shared Task on Prompt Recovery for LLM in Telugu at DravidianLangTech @ ACL{~}2026, which aims to classify Telugu transcript excerpts into nine communicative style categories: Formal, Informal, Optimistic, Pessimistic, Humorous, Serious, Inspiring, Authoritative, and Persuasive.We have implemented a transformer-based approach using ai4bharat/IndicBERTv2-MLM-only, MuRIL-base and Telugu-BERT for Telugu communicative style classification. Our system fine-tunes the pretrained Indic language training samples to capture stylistic patterns in Telugu transcripts. Our approach achieved a macro F1 score of 0.2993 on the evaluation set, demonstrating the potential of Indic-focused pretrained models for stylistic analysis in low-resource language settings.Controlled ablations reveal that label smoothing benefits stronger Indic backbones but degrades weaker ones, and that surface linguistic feature augmentation does not complement rich contextual representations on small datasets."
}