发表机构
Wichita State University（威奇托州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究人员提出TRACE框架，通过强化检索提升公共服务对话系统的约束满足度、减少幻觉，降低对模型规模的依赖，证实检索质量是系统稳健性的关键。
AI 中文摘要
公共服务聊天机器人需基于底层公共服务目录提供推荐，同时确保推荐符合用户明确约束。但实际中公共服务目录存在噪声且不一致，通用大语言模型（LLM）或AI聊天机器人常生成不可靠推荐，引用未验证的网络来源。我们研究检索质量对基于噪声异构服务目录构建的公共服务对话系统中约束感知推荐的影响，提出TRACE（Trustworthy Retrieval-Augmented Conversational Engine），这是一种基于检索的约束感知框架，借助双数据表示模式将用户输入查询解析为结构和语义约束以用于下游检索。我们使用整理后的全州食品储藏室目录和合成查询基准，评估多种带或不带知识图谱（KG）的知识表示变体，对多个开源LLM和一个专有模型开展实验，结果表明强化检索可显著提升用户约束满足度，减少幻觉推荐；实验中随着检索质量提升，不同LLM的性能差异缩小，使结果对模型规模的敏感性降低。这些发现说明检索质量是构建稳健公共服务对话系统的关键。
英文摘要
Public service chatbots are expected to deliver recommendations from an underlying public service directory, while also making sure that the recommendations respect explicit user constraints. In practice, public service directories are noisy and inconsistent, and general-purpose large language model (LLM) or AI-based chatbots frequently generate unreliable recommendations, citing unverified sources from the web. We investigate the impact of retrieval quality on constraint-aware recommendation in public service conversational systems built over noisy and heterogeneous service directories. We propose TRACE (Trustworthy Retrieval-Augmented Conversational Engine), a retrieval-based, constraint-aware framework that parses input user queries into structural and semantic constraints for downstream retrieval, with the help of a dual data representation schema. Using a curated statewide pantry directory and a synthetic query benchmark, we evaluate multiple knowledge-representation variants with and without knowledge graphs (KGs). We experiment with several open-source LLMs and a proprietary model, showing that strengthening retrieval substantially improves user constraint satisfaction while reducing hallucinated recommendations. Performance differences across LLMs narrowed in our experiments as retrieval quality improved, making results less sensitive to model size. These findings suggest that the quality of retrieval is key for robust public service conversational systems.
