@inproceedings{satyadharma-etal-2025-auto,
title = "Auto prompting without training labels: An {LLM} cascade for product quality assessment in e-commerce catalogs",
author = "Satyadharma, Soham and
Sheikholeslami, Fatemeh and
Kaul, Swati and
Batur, Aziz Umit and
Khan, Suleiman A.",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.63/",
pages = "937--953",
ISBN = "979-8-89176-333-3",
abstract = "We introduce a novel, training free cascade for auto-prompting Large Language Models (LLMs) to assess product quality in e-commerce. Our system requires no training labels or model fine-tuning, instead automatically generating and refining prompts for evaluating attribute quality across tens of thousands of product category{--}attribute pairs. Starting from a seed of human-crafted prompts, the cascade progressively optimizes instructions to meet catalog-specific requirements. This approach bridges the gap between general language understanding and domain-specific knowledge at scale in complex industrial catalogs. Our extensive empirical evaluations shows the auto-prompt cascade improves precision and recall by 8{--}10{\%} over traditional chain-of-thought prompting. Notably, it achieves these gains while reducing domain expert effort from 5.1 hours to 3 minutes per attribute - a 99{\%} reduction. Additionally, the cascade generalizes effectively across five languages and multiple quality assessment tasks, consistently maintaining performance gains."
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%0 Conference Proceedings
%T Auto prompting without training labels: An LLM cascade for product quality assessment in e-commerce catalogs
%A Satyadharma, Soham
%A Sheikholeslami, Fatemeh
%A Kaul, Swati
%A Batur, Aziz Umit
%A Khan, Suleiman A.
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F satyadharma-etal-2025-auto
%X We introduce a novel, training free cascade for auto-prompting Large Language Models (LLMs) to assess product quality in e-commerce. Our system requires no training labels or model fine-tuning, instead automatically generating and refining prompts for evaluating attribute quality across tens of thousands of product category–attribute pairs. Starting from a seed of human-crafted prompts, the cascade progressively optimizes instructions to meet catalog-specific requirements. This approach bridges the gap between general language understanding and domain-specific knowledge at scale in complex industrial catalogs. Our extensive empirical evaluations shows the auto-prompt cascade improves precision and recall by 8–10% over traditional chain-of-thought prompting. Notably, it achieves these gains while reducing domain expert effort from 5.1 hours to 3 minutes per attribute - a 99% reduction. Additionally, the cascade generalizes effectively across five languages and multiple quality assessment tasks, consistently maintaining performance gains.
%U https://aclanthology.org/2025.emnlp-industry.63/
%P 937-953
Markdown (Informal)
[Auto prompting without training labels: An LLM cascade for product quality assessment in e-commerce catalogs](https://aclanthology.org/2025.emnlp-industry.63/) (Satyadharma et al., EMNLP 2025)
ACL