Cleo：一款用于会话式商务的透明且可控制的聊天机器人
Cleo: A Transparent and Controllable Chatbot for Conversational Commerce
AI总结：
Cleo是一款用于会话式商务的透明可控制聊天机器人，通过混合架构、可审计排序机制等解决LLM的不透明性等问题，为产品搜索决策提供支持。
AI中文摘要：
我们展示了Cleo，这是一款透明且可控制的会话式产品顾问，旨在解决会话式商务中大型语言模型（LLM）的不透明性、不可预测性以及产品比较复杂性等挑战。该聊天机器人系统有四项贡献：第一，我们通过提示LLM对已解析的用户需求进行反思来实现透明性，同时可审计的排序机制会显示每个属性的损失值，以此解释排序决策。第二，我们提出了一种混合架构来实现可控性，该架构将确定性排序与语言生成分离。排序器对3638个产品规格应用分类筛选和数值损失函数；同时，受约束的LLM会生成基于目录证据的描述，从而减轻幻觉或说服性内容的风险。第三，我们提供自然语言比较和亮点功能形式的决策支持，这些功能旨在通过将规格与用户需求关联起来，减少用户的心理负担。第四，我们为信息检索（IR）、人机交互（HCI）研究人员以及会话搜索和推荐领域的从业者提供了一个可扩展的实验系统。与传统的分面搜索或仅基于LLM的不透明推荐系统不同，我们的方法既能实现流畅的对话，又能保持算法透明性。在现场演示中，参与者将体验信息需求的引出与反思、带有实时重新排序的对话式优化、各属性损失解释的检查，以及AI生成的多产品比较。该系统旨在推进透明且可控制的会话系统的设计，为在线产品搜索过程中的决策提供支持。
英文摘要：
We demonstrate Cleo, a transparent and controllable conversational product advisor that addresses the challenges of opacity, unpredictability of LLMs, and the complexity of comparisons in conversational commerce. With our chatbot system, we make four contributions: First, we introduce transparency by prompting the LLM to reflect on interpreted user needs, while an auditable ranking mechanism reveals loss values per attribute, explaining ranking decisions. Second, we propose controllability through a hybrid architecture separating deterministic ranking from language generation. A ranker applies categorical filters and numeric loss functions over 3,638 product specifications. Meanwhile, a constrained LLM generates grounded descriptions constrained to catalog evidence, thus mitigating the risk of hallucinated or persuasive content. Third, we provide decision support in the form of natural-language comparisons and a highlights feature. These aim to reduce mental workload by contextualizing specifications relative to user needs. Fourth, we contribute an extensible experimental system for IR and HCI researchers, as well as practitioners of conversational search and recommendation. Unlike traditional faceted search or opaque LLM-only recommenders, our approach allows for fluid conversation while maintaining algorithmic transparency. In a live demonstration, attendees will experience information needs elicitation and reflection, conversational refinement with real-time re-ranking, inspection of per-attribute loss explanations, and AI-generated multi-item comparisons. The system aims to advance the design of transparent and controllable conversational systems that provide support for decision-making during online product search.
