Progressive Fine-Tuning for Cost-Effective Structured Attribute Generation in E-commerce (2026.acl-industry)
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| Challenge: | Large language models excel at structured information generation but face cost and latency challenges when deployed at scale in user-facing products. |
| Approach: | They propose a parameter efficient supervised fine-tuning pipeline for adapting a small language model to structured attribute generation in e-commerce product listing. |
| Outcome: | The proposed model reduces inference costs by 98% and latency by 70% on a large-scale product listing service while preserving an 86.4% user acceptance rate. |
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