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arXiv Computation and Language By Nikhita Vedula, Dushyanta Dhyani, Bryan Wang, Shervin Malmasi

Scaling E-Commerce Attribute Extraction with Parallel Decoding

Read the original on arXiv Computation and Language →

The paper presents a two‑stage large language model pipeline for e‑commerce attribute extraction. First, it discovers a compact, ranked set of purchase‑discriminative attributes for each product category; second, it extracts those attribute values from catalog text using a fine‑tuned Qwen3‑4B model with Hyper‑Parallel Decoding. The approach attains 85% extraction accuracy while cutting inference costs by 92%, enabling scalable production use and producing consistent, comparable product knowledge bases.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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