HINTs: Sensemaking on large collections of documents with Hypergraph visualization and INTelligent agents
<|reference_start|>HINTs: Sensemaking on large collections of documents with Hypergraph visualization and INTelligent agents: Sensemaking on a large collection of documents (corpus) is a challenging task often found in fields such as market research, legal studies, intelligence analysis, political science, computational linguistics, etc. Previous works approach this problem either from a topic- or entity-based perspective, but they lack interpretability and trust due to poor model alignment. In this paper, we present HINTs, a visual analytics approach that combines topic- and entity-based techniques seamlessly and integrates Large Language Models (LLMs) as both a general NLP task solver and an intelligent agent. By leveraging the extraction capability of LLMs in the data preparation stage, we model the corpus as a hypergraph that matches the user's mental model when making sense of the corpus. The constructed hypergraph is hierarchically organized with an agglomerative clustering algorithm by combining semantic and connectivity similarity. The system further integrates an LLM-based intelligent chatbot agent in the interface to facilitate sensemaking. To demonstrate the generalizability and effectiveness of the HINTs system, we present two case studies on different domains and a comparative user study. We report our insights on the behavior patterns and challenges when intelligent agents are used to facilitate sensemaking. We find that while intelligent agents can address many challenges in sensemaking, the visual hints that visualizations provide are necessary to address the new problems brought by intelligent agents. We discuss limitations and future work for combining interactive visualization and LLMs more profoundly to better support corpus analysis.<|reference_end|>
@article{lee2024hints:,
    title={HINTs: Sensemaking on large collections of documents with Hypergraph
  visualization and INTelligent agents},
    author={Sam Yu-Te Lee, Kwan-Liu Ma},
    journal={arXiv preprint arXiv:2403.02752},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02752},
    primaryClass={cs.HC}
}