让大模型用10+工具，更真实地回答用户推荐需求。
OMuleT: Orchestrating Multiple Tools for Practicable Conversational Recommendation
- 用10多个工具增强大模型，支持真实语境下的推荐请求。
- 在真实用户数据上验证，推荐结果更相关、新颖且多样。
- 提供可落地部署的经验，适合做对话推荐系统的开发者参考。
本文系统设计、评估并实现了一个贴近实际的对话式推荐系统（CRS）。目标是让用户以自由文本形式提出推荐请求，并获得相关且多样化的物品列表。以往研究仅用1-3个工具增强大语言模型（LLMs），而我们认为，要有效处理真实用户请求，需更广泛的工具支持。因此，我们提出一种新方法，为LLMs配备超过10个工具，使其可访问生产环境中的内部知识库和API调用。我们在真实用户数据集上评估该模型，结果显示其生成的推荐比基础LLM更具相关性、新颖性和多样性。此外，消融实验验证了使用全部工具的有效性。我们分享了系统内部测试阶段的设计与经验。本工作覆盖了实用化对话推荐系统的四大关键方面：(1) 真实用户请求，(2) 多样化工具增强LLM，(3) 全面评估，(4) 部署洞察。
原文摘要 · Abstract (English)
In this paper, we present a systematic effort to design, evaluate, and implement a realistic conversational recommender system (CRS). The objective of our system is to allow users to input free-form text to request recommendations, and then receive a list of relevant and diverse items. While previous work on synthetic queries augments large language models (LLMs) with 1-3 tools, we argue that a more extensive toolbox is necessary to effectively handle real user requests. As such, we propose a novel approach that equips LLMs with over 10 tools, providing them access to the internal knowledge base and API calls used in production. We evaluate our model on a dataset of real users and show that it generates relevant, novel, and diverse recommendations compared to vanilla LLMs. Furthermore, we conduct ablation studies to demonstrate the effectiveness of using the full range of tools in our toolbox. We share our designs and lessons learned from deploying the system for internal alpha release. Our contribution is the addressing of all four key aspects of a practicable CRS: (1) real user requests, (2) augmenting LLMs with a wide variety of tools, (3) extensive evaluation, and (4) deployment insights.
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