OpinionConv: Conversational Product Search with Grounded Opinions
원문 초록
When searching for products, the opinions of others play an important role in making informed decisions. Subjective experiences about a product can be a valuable source of information. This is also true in sales conversations, where a customer and a sales assistant exchange facts and opinions about products. However, training an AI for such conversations is complicated by the fact that language models do not possess authentic opinions for their lack of real-world experience. We address this problem by leveraging product reviews as a rich source of product opinions to ground conversational AI in true subjective narratives. With OpinionConv, we develop the first conversational AI for simulating sales conversations. To validate the generated conversations, we conduct several user studies showing that the generated opinions are perceived as realistic. Our assessors also confirm the importance of opinions as an informative basis for decision-making.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Graph-Structured Data Analysis of Component Failure in Autonomous Cargo Ships Based on Feature Fusion
Guiding LLM Decision-Making with Fairness Reward Models
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior
VERITAS: Verification and Explanation of Realness in Images for Transparency in AI Systems
Enhancing LLM Agent Safety via Causal Influence Prompting
NavMorph: A Self-Evolving World Model for Vision-and-Language Navigation in Continuous Environments
토론
이 논문과 연결된 포럼 글이 아직 없습니다.