Authority Aware RAG for Parliamentary Transcripts

Summary & Methodology Analysis

ParliamentRAG enhances standard Retrieval-Augmented Generation (RAG), which is an architecture that fetches external documents to ground LLM responses, by incorporating an authority-aware layer. Rather than retrieving chunks based solely on vector similarity, the system calculates authority scores for each speaker. This calculation considers the user query alongside metadata including the speaker's profession, educational history, and record of past parliamentary interventions. By doing so, the framework identifies topic-relevant experts across diverse political groups before initiating the synthesis of responses. This approach specifically targets the risks inherent in standard implementations, such as the dominance of frequent speakers and the common failure of accurate citation attribution. The system then generates a summary that synthesizes these varied perspectives while enforcing the inclusion of verified quotations. This is a significant shift from generic retrieval pipelines, as it moves the focus from raw semantic similarity to identifying credible, domain-specific sources within a sensitive dataset. Despite these gains in retrieval and quotation precision, the current implementation has specific performance trade-offs. The authors note that Google NotebookLM remains superior in terms of prose-oriented dimensions, suggesting that while the retrieval logic is improved, the final natural language generation quality does not yet surpass the baseline for readability or style. The paper does not specify the computational overhead, inference latency, or the total parameter count of the underlying models used for these evaluations.