Who Speaks Matters: Authority-Aware Multi-View RAG over Italian Parliamentary Proceedings· 谁发言很重要：权威感知的多视角 RAG
Parliamentary proceedings are a primary record of democratic deliberation, yet their volume and fragmentation make multi-perspective access difficult for citizens, journalists, and researchers. Applying Retrieval-Augmented Generation (RAG) to parliamentary transcripts introduces three specific risks: dominance of the most frequent speakers, inability to weight speakers according to topical expertise, and citation misattribution in politically sensitive text. We present ParliamentRAG, a RAG system for the Italian Chamber of Deputies that addresses these risks jointly. Its core contribution is a topic-dependent authority model that estimates each speaker's authority as a function of the current query, combining interpretable components such as profession, education, and previous intervention
提出权威感知的多视角 RAG 系统，优化意大利议会记录的多视角访问。
- 核心方法
- 设计了一个主题依赖的权威模型，根据当前查询估计每个发言者的权威性，结合了职业、教育背景和先前发言等可解释性组件。
- 适合谁读
- 研究者、工程师、政治分析师
- 要解决的问题
- 解决在大量议会记录数据中，检索生成系统可能被最频繁发言者主导、无法根据话题专长加权发言者以及敏感文本引用错误的问题。
- 关键实验
- 在意大利下议院的议会记录上进行了实验，验证了系统在多视角和权威感知方面的有效性，但具体实验数据未详细提供。
- 主要贡献
- 开发了 ParliamentRAG 系统，能够更公正和准确地处理意大利议会记录，提供多视角访问。
- 意义与局限
- 提高了公民、记者和研究人员对议会记录的访问质量，但系统可能受限于特定国家和语言的适用性。