# HINTs: Sensemaking on large collections of documents with Hypergraph visualization and INTelligent agents arXiv: https://arxiv.org/abs/2403.02752 ยท HTML: https://mlsanity.com/paper/2403.02752/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. - Builds on 28 of its 80 references that are in the map. - First posted on arXiv 5 Mar 2024. ## Placed in - LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/) ## Builds on (most cited first, 28 of 28) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020) - [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021) - [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023) - [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023) - [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022) - [Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness](https://mlsanity.com/paper/2304.11633/) (2023) - [Context-faithful Prompting for Large Language Models](https://mlsanity.com/paper/2303.11315/) (2023) - [ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking](https://mlsanity.com/paper/2207.04108/) (2022) - [Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making](https://mlsanity.com/paper/2010.07938/) (2020) - [Chat2VIS: Generating Data Visualisations via Natural Language using ChatGPT, Codex and GPT-3 Large Language Models](https://mlsanity.com/paper/2302.02094/) (2023) - [Extractive Summarization via ChatGPT for Faithful Summary Generation](https://mlsanity.com/paper/2304.04193/) (2023) - [A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective](https://mlsanity.com/paper/2304.08795/) (2023) - [Data Formulator: AI-powered Concept-driven Visualization Authoring](https://mlsanity.com/paper/2309.10094/) (2023) - [LLMs as Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines with LLMs](https://mlsanity.com/paper/2307.10168/) (2023) - [VitaLITy: Promoting Serendipitous Discovery of Academic Literature with Transformers & Visual Analytics](https://mlsanity.com/paper/2108.03366/) (2021) - [Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization](https://mlsanity.com/paper/2307.11770/) (2023) - [Towards a Survey on Static and Dynamic Hypergraph Visualizations](https://mlsanity.com/paper/2107.13936/) (2021) - [Cartolabe: A Web-Based Scalable Visualization of Large Document Collections](https://mlsanity.com/paper/2003.00975/) (2020) - [wTO: an R package for computing weighted topological overlap and consensus networks with an integrated visualization tool](https://mlsanity.com/paper/1711.04702/) (2017) - [Eva: Attribute-Aware Network Segmentation](https://mlsanity.com/paper/1910.06599/) (2019) - [Through the Looking Glass: Insights into Visualization Pedagogy through Sentiment Analysis of Peer Review Text](https://mlsanity.com/paper/2108.02839/) (2021) - [SQRQuerier: A Visual Querying Framework for Cross-national Survey Data Recycling](https://mlsanity.com/paper/2201.11125/) (2022) - [Networks of Collaborations: Hypergraph Modeling and Visualisation](https://mlsanity.com/paper/1707.00115/) (2017) - [HisVA: A Visual Analytics System for Studying History](https://mlsanity.com/paper/2106.00764/) (2021) - [Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations](https://mlsanity.com/paper/2208.04458/) (2022) - [Scalable Hypergraph Visualization](https://mlsanity.com/paper/2308.05043/) (2023) ## Built on by (most cited first, 2 of 2) - [Completing A Systematic Review in Hours instead of Months with Interactive AI Agents](https://mlsanity.com/paper/2504.14822/) (2025) - [RAGExplorer: A Visual Analytics System for the Comparative Diagnosis of RAG Systems](https://mlsanity.com/paper/2601.12991/) (2026) ## Abstract (arXiv) 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.