@inproceedings{sai-etal-2026-bytebreaker,
    title = "{B}yte{B}reaker@{D}ravidian{L}ang{T}ech 2026: {XLM}-{R}o{BERT}a Large with Sliding-Window Chunking and Top-K Mean Pooling for Writing Style Classification",
    author = "Sai, Chava Srinivasa  and
      Kumar, R Vinay  and
      Surapaneni, Jigeesha Sai  and
      Sai, Chava Shanmukha",
    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-1.19/",
    pages = "158--162",
    ISBN = "979-8-89176-401-9",
    abstract = "Identifying different writing styles in large chunks of text is difficult because writing styles vary in different sections of a document. Additionally, the writing styles associated with a text can be differentiated in only tiny and nuanced ways. In this paper, we describe ByteBreaker, the system we built for the Prompt Recovery for LLM Shared Task at DravidianLangTech@ACL-2026. The goal is to analyze the writing style in a specific document that a large language model (LLM) has written. The styles to choose from are categorized as: Authoritative,Formal, Humorous,Informal,Inspiring,Optimistic,Persuasive, Pessimistic, and Serious. Given that a number of documents exceed the 512 token limit of transformer models, we adopt a sliding-window method that breaks each document down into overlapping 512 token chunks, with a stride of 256 tokens. We fine-tune XLM-RoBERTa Large with just the rewritten ``CHANGE STYLE'' text, as that one has more distinct stylistic indicators. For prediction, we Top-K mean pool the chunk-level predictions, which puts more emphasis on the confident chunks as opposed to treating all chunks the same. To enhance consistency, we trained the model with five distinct random seeds and made three submission:a weighted ensemble(Run 1), a mean-guided single model (Run 2), and a Top-K-guided single model (Run 3). Among the three, Run 3 reached the highest macro F1 score of 0.3306, while Run 1 achieves the best accuraccy(0.3256) with a macro F1 of 0.3290."
}