Lakshman Kolasani
2026
Progressive Fine-Tuning for Cost-Effective Structured Attribute Generation in E-commerce
Lakshman Kolasani | Fatemeh Taheri Dezaki
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
Lakshman Kolasani | Fatemeh Taheri Dezaki
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
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.