mlsanityData to 14 Oct 2026

Auto prompting without training labels: An LLM cascade for product quality assessment in e-commerce catalogs

arXiv 2510.23941 ↗First posted 27 Oct 2025cs.CL cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 17 of its 36 references that are in the map. First posted on arXiv 27 Oct 2025.

Builds on

17 of 36 references.

References · 17

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  3. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  4. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,045
  5. 2022RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learningcited by 366
  6. 2024Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programscited by 291
  7. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 73
  8. 2024PRewrite: Prompt Rewriting with Reinforcement Learningcited by 45
  9. 2024Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Togethercited by 40
  10. 2023Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMscited by 24
  11. 2024Using LLMs for the Extraction and Normalization of Product Attribute Valuescited by 20
  12. 2023Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extractioncited by 19
5 more
  1. 2024ARES: Alternating Reinforcement Learning and Supervised Fine-Tuning for Enhanced Multi-Modal Chain-of-Thought Reasoning Through Diverse AI Feedbackcited by 13
  2. 2024Leveraging Large Language Models for Enhanced NLP Task Performance through Knowledge Distillation and Optimized Training Strategiescited by 6
  3. 2020Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Datacited by 5
  4. 2024Learning variant product relationship and variation attributes from e-commerce website structurescited by 4
  5. 2025A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Modelscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026CatalogAgent: A Supervisor-mediated Self-Learning System Enabling Context Engineering for GenAI Modelscited by 0
  2. 2026When Do LLMs Actually Help? Evaluating LLMs as Data Quality Annotatorscited by 0
  3. 2026Retrieve, Match, Escalate: Accurate and Scalable Product Linking with VLM-Distilled Cross-Encoders and Agentic VLMscited by 0
  4. 2026Positional task conditioning for scalable defect detection across product families in large product catalogscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023PUMGPT: A Large Vision-Language Model for Product Understanding
  2. 2024Retrieve, Annotate, Evaluate, Repeat: Leveraging Multimodal LLMs for Large-Scale Product Retrieval Evaluation
  3. 2025Automated Query-Product Relevance Labeling using Large Language Models for E-commerce Search
  4. 2026The Differential Reasoning Router: Operationalizing Cost-Aware LLM Annotation in E-commerce
  5. 2025AutoQual: An LLM Agent for Automated Discovery of Interpretable Features for Review Quality Assessment
  6. 2023Little Giants: Exploring the Potential of Small LLMs as Evaluation Metrics in Summarization in the Eval4NLP 2023 Shared Task
  7. 2026Adapting Vision-Language Models for E-commerce Understanding at Scale
  8. 2024Generating Attractive and Authentic Copywriting from Customer Reviews