mlsanityData to 7 Oct 2026

Question Suggestion for Conversational Shopping Assistants Using Product Metadata

arXiv 2405.01738 ↗First posted 2 May 2024cs.CLcited by 8

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Abstract (arXiv)

Digital assistants have become ubiquitous in e-commerce applications, following the recent advancements in Information Retrieval (IR), Natural Language Processing (NLP) and Generative Artificial Intelligence (AI). However, customers are often unsure or unaware of how to effectively converse with these assistants to meet their shopping needs. In this work, we emphasize the importance of providing customers a fast, easy to use, and natural way to interact with conversational shopping assistants. We propose a framework that employs Large Language Models (LLMs) to automatically generate contextual, useful, answerable, fluent and diverse questions about products, via in-context learning and supervised fine-tuning. Recommending these questions to customers as helpful suggestions or hints to both start and continue a conversation can result in a smoother and faster shopping experience with reduced conversation overhead and friction. We perform extensive offline evaluations, and discuss in detail about potential customer impact, and the type, length and latency of our generated product questions if incorporated into a real-world shopping assistant.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 11 of its 23 references that are in the map. First posted on arXiv 2 May 2024.

Builds on

11 of 23 references.

References · 11

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  5. 2023Efficient Streaming Language Models with Attention Sinkscited by 2,115
  6. 2023The Flan Collection: Designing Data and Methods for Effective Instruction Tuningcited by 734
  7. 2023Instruction Tuning for Large Language Models: A Surveycited by 617
  8. 2023A Survey of Hallucination in Large Foundation Modelscited by 365
  9. 2023ToolQA: A Dataset for LLM Question Answering with External Toolscited by 273
  10. 2022Question Generation for Reading Comprehension Assessment by Modeling How and What to Askcited by 20
  11. 2023Product Question Answering in E-Commerce: A Surveycited by 10

Built on by

Cited by year

Citing papers per year: 2024: 5; 2025: 3.

8 papers.

  1. 2024ProductAgent: Benchmarking Conversational Product Search Agent with Asking Clarification Questionscited by 10
  2. 2024Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using LLM Optimizerscited by 7
  3. 2025Generative Product Recommendations for Implicit Superlative Queriescited by 5
  4. 2024Bridging the Gap Between Information Seeking and Product Search Systems: Q&A Recommendation for E-commercecited by 4
  5. 2024Identifying High Consideration E-Commerce Search Queriescited by 4
  6. 2024Research on Tibetan Tourism Viewpoints information generation system based on LLMcited by 1
  7. 2025PSCon: Product Search Through Conversationscited by 1
  8. 2025Hint-Augmented Re-ranking: Efficient Product Search using LLM-Based Query Decompositioncited by 1