@inproceedings{kolasani-dezaki-2026-progressive,
    title = "Progressive Fine-Tuning for Cost-Effective Structured Attribute Generation in {E}-commerce",
    author = "Kolasani, Lakshman  and
      Dezaki, Fatemeh Taheri",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    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, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.40/",
    pages = "585--591",
    ISBN = "979-8-89176-394-4",
    abstract = "Large language models (LLMs) excel at structured information generation but face cost and latency challenges when deployed at scale in user-facing products. We present a parameter efficient supervised fine-tuning pipeline for adapting a small language model (SLM) to structured attribute generation in e-commerce product listing, enabling continuous model improvement with implicit user feedback without expensive manual annotation. Our approach involves completeness-deficit guided curation, which ranks samples by divergence between model predictions and catalog listing attributes, selecting the highest completeness gap examples for progressive fine-tuning. Our system is deployed on a large-scale product listing service, reducing inference costs by 98{\%} and p90 latency by 70{\%} using a fine-tuned SLM relative to the baseline LLM while preserving an 86.4{\%} user acceptance rate, translating to significant monthly infrastructure savings."
}