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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2510.23941 |
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| _version_ | 1866911236388552704 |
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| author | Satyadharma, Soham Sheikholeslami, Fatemeh Kaul, Swati Batur, Aziz Umit Khan, Suleiman A. |
| author_facet | Satyadharma, Soham Sheikholeslami, Fatemeh Kaul, Swati Batur, Aziz Umit Khan, Suleiman A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23941 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Auto prompting without training labels: An LLM cascade for product quality assessment in e-commerce catalogs Satyadharma, Soham Sheikholeslami, Fatemeh Kaul, Swati Batur, Aziz Umit Khan, Suleiman A. Computation and Language Artificial Intelligence 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. |
| title | Auto prompting without training labels: An LLM cascade for product quality assessment in e-commerce catalogs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.23941 |