Organizations: University of Milano-Bicocca, Milan, Italy
Abstract
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 interventions. Given a user query, the system retrieves relevant speech chunks, identifies topic-relevant experts across parliamentary groups, and generates a summary synthesizing their perspectives, accompanied by supporting quotations. ParliamentRAG is evaluated against Google NotebookLM on 15 policy topics via a two-level protocol combining automated metrics and blind A/B human evaluation by six domain experts. The system achieves higher coverage across political groups (0.97 vs. 0.95), perfect quotation faithfulness (1.00 vs. 0.95), and stronger expert preferences on source-related dimensions, while NotebookLM remains stronger on prose-oriented dimensions.
Analyses of legislative behavior often rely on voting records, overlooking the rich semantic and rhetorical content of political speech. In this paper, we ask three complementary questions about parliamentary discourse: how things are said, what is being said, and who is speaking in discursively similar ways. To answer these questions, we introduce a scalable and generalizable computational framework that combines diachronic stylometric analysis, contextual topic modeling, and semantic clustering of deputies' speeches. We apply this framework to a large-scale case study of the Brazilian Chamber of Deputies, using a corpus of over 450,000 speeches from 2003 to 2025. Our results show a long-term stylistic shift toward shorter and more direct speeches, a legislative agenda that reorients sharply in response to national crises, and a granular map of discursive alignments in which regional and gender identities often prove more salient than formal party affiliation. More broadly, this work offers a robust methodology for analyzing parliamentary discourse as a multidimensional phenomenon that complements traditional vote-based approaches.
Flávio Soriano, Victoria F. Mello, Pedro B. Rigueira +4
In this paper, we evaluate the extent of undisclosed LLM-generated content in texts from the parliaments of the United Kingdom and Sweden. In many areas, such as in journalism or in academic writing, there are often requirements to clearly disclose whether AI tools, such as LLMs, have been used. In the case of parliamentary texts, the guidelines on disclosure of AI use are more vague. However, in order to maintain transparency and retain public trust, it is generally recommended that parliamentarians should state whether or not they have used AI when writing texts, such as parliamentary motions. Here, we train an interpretable (glass-box) text classifier using pre-LLM parliamentary texts and LLM-generated versions of such texts. We then apply the classifier to a test set containing recent parliamentary texts, finding a steady increase in undisclosed LLM use, in both parliaments, from 2022 onwards.
Minerva Suvanto, Andrea McGlinchey, Peter J. Barclay +1
Retrieval-Augmented Generation (RAG) improves large language models by grounding them in external evidence, but this exposes them to knowledge-poisoning attacks, where misinformation injected into retrieved documents influences model outputs. We investigate whether deliberative reasoning reduces susceptibility to poisoned evidence using two metrics: Cordon Rate, which measures cases where detected misinformation nevertheless influences the final answer, and Leakage Rate, which measures implicit influence from poisoned context despite explicit instructions to disregard it. We evaluate six model configurations on 200 SciFact questions, including DeepSeek-V4-Flash and Qwen3.6-Plus with reasoning disabled and enabled. Enabling reasoning reduces conditional susceptibility: DeepSeek-V4-Flash reduces Cordon Rate from 0.211 to 0.107 and Leakage Rate from 0.235 to 0.140, despite overall attack success rising from 0.233 to 0.298. These results show that poison detection, attack success, and resistance to contextual influence are distinct capabilities, and that deliberative reasoning reduces behavioral impact of corrupted evidence conditional on detection, even as it renders explicit poison identification less reliable.
Mehrdad Ghassabi, Audrina Ebrahimi, Sadra Hakim +1