Abstract
KamerRaad is an AI tool that leverages large language models to help citizens interactively engage with Belgian political information. The tool extracts and concisely summarizes key excerpts from parliamentary proceedings, followed by the potential for interaction based on generative AI that allows users to steadily build up their understanding. KamerRaad’s front-end, built with Streamlit, facilitates easy interaction, while the back-end employs open-source models for text embedding and generation to ensure accurate and relevant responses. By collecting feedback, we intend to enhance the relevancy of our source retrieval and the quality of our summarization, thereby enriching the user experience with a focus on source-driven dialogue.
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Acknowledgments
The research leading to these results has received funding from the Special Research Fund (BOF) of Ghent University (BOF20/IBF/117), from the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie Vlaanderen” programme, and from the FWO (project no. G0F9816N, 3G042220, G073924N).
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Rogiers, A., Buyl, M., Kang, B., De Bie, T. (2024). KamerRaad: Enhancing Information Retrieval in Belgian National Politics Through Hierarchical Summarization and Conversational Interfaces. In: Bifet, A., et al. Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track. ECML PKDD 2024. Lecture Notes in Computer Science(), vol 14948. Springer, Cham. https://doi.org/10.1007/978-3-031-70371-3_30
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DOI: https://doi.org/10.1007/978-3-031-70371-3_30
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