View a PDF of the paper titled VIDEE: Visual and Interactive Decomposition, Execution, and Evaluation of Text Analytics with Intelligent Agents, by Sam Yu-Te Lee and 5 other authors
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Abstract:Text analytics has traditionally required specialized knowledge in Natural Language Processing (NLP) or text analysis, which presents a barrier for entry-level analysts. Recent advances in large language models (LLMs) have changed the landscape of NLP by enabling more accessible and automated text analysis (e.g., topic detection, summarization, information extraction, etc.). We introduce VIDEE, a system that supports entry-level data analysts to conduct advanced text analytics with intelligent agents. VIDEE instantiates a human-agent collaboration workflow consisting of three stages: (1) Decomposition, which incorporates a human-in-the-loop Monte-Carlo Tree Search algorithm to support generative reasoning with human feedback, (2) Execution, which generates an executable text analytics pipeline, and (3) Evaluation, which integrates LLM-based evaluation and visualizations to support user validation of execution results. We conduct two quantitative experiments to evaluate VIDEE’s effectiveness and analyze common agent errors. A user study involving participants with varying levels of NLP and text analytics experience — from none to expert — demonstrates the system’s usability and reveals distinct user behavior patterns. The findings identify design implications for human-agent collaboration, validate the practical utility of VIDEE for non-expert users, and inform future improvements to intelligent text analytics systems.
Submission history
From: YuTe Lee [view email]
[v1] Tue, 17 Jun 2025 05:24:58 UTC (2,128 KB)
[v2] Thu, 17 Jul 2025 03:52:15 UTC (2,128 KB)
[v3] Wed, 10 Sep 2025 02:31:17 UTC (2,101 KB)
Understanding VIDEE: A Breakthrough in Text Analytics
In a world increasingly dominated by data, the ability to derive meaningful insights from text has never been more critical. Traditionally, text analytics has been the realm of experts who possess sophisticated knowledge of Natural Language Processing (NLP) and data analysis. However, a groundbreaking system known as VIDEE aims to democratize access to text analytics for entry-level data analysts.
The Landscape of Text Analytics
As businesses and individuals generate vast amounts of textual data daily, the need for effective text analytics solutions has surged. NLP, once a niche area of expertise, is transforming with advancements in large language models (LLMs). These technologies simplify tasks such as topic detection, summarization, and information extraction, making them more accessible. Yet, challenges persist, particularly for those new to the field. VIDEE addresses this by providing a structured system that enhances human-agent collaboration and streamlines the process.
Key Features of VIDEE
1. Decomposition Stage:
At the heart of VIDEE lies its innovative three-stage workflow, starting with the Decomposition stage. Here, a human-in-the-loop Monte-Carlo Tree Search algorithm comes into play. This feature not only assists in breaking down complex tasks into manageable components but also allows for generative reasoning. By incorporating human feedback within this algorithm, users can fine-tune their analytical process, enhancing both efficiency and understanding.
2. Execution Stage:
Once tasks are decomposed, VIDEE transitions into the Execution stage. This phase focuses on generating an executable text analytics pipeline, facilitating a seamless transition from conceptual analysis to practical application. In effect, users can deploy sophisticated analytical methods without needing an advanced background in NLP.
3. Evaluation Stage:
The final stage, Evaluation, brings together LLM-based assessments and visualizations to validate the outcomes. This helps users confirm the accuracy and relevance of their analysis, ultimately boosting confidence in the results. The visual elements also aid in understanding complex data relationships, making findings easier to interpret and present.
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Empirical Evidence of Usability
To gauge the effectiveness of VIDEE, researchers conducted two quantitative experiments, meticulously evaluating its performance and identifying common errors made by the intelligent agents. In addition, a user study was carried out to include participants varying from complete beginners to seasoned experts in NLP and text analytics.
The results were telling: not only did users of all experience levels find VIDEE intuitive and easy to navigate, but the study also uncovered unique behavior patterns. These insights are invaluable for refining human-agent collaboration further and enhancing user experience.
Practical Implications and Future Directions
The implications of VIDEE extend beyond just usability. The study’s findings reveal critical design considerations for future intelligent text analytics systems. Notably, they emphasize the importance of creating user-centric designs that accommodate different levels of expertise. The knowledge gained through this research not only validates the utility of VIDEE for novices but also guides strategic improvements for forthcoming iterations.
It’s clear that with systems like VIDEE, the future of text analytics holds great promise. By bridging the gap between advanced analytical techniques and accessibility, VIDEE empowers individuals to transform raw text into actionable insights efficiently. The journey of enhancing human-agent collaboration in text analytics has just begun, but the groundwork laid by VIDEE is undoubtedly a significant step in the right direction.
The Submission Process
For those interested in exploring the full study, the paper titled "VIDEE: Visual and Interactive Decomposition, Execution, and Evaluation of Text Analytics with Intelligent Agents" is available in a PDF format. Since its submission on June 17, 2025, multiple revisions have fine-tuned the document, with the latest version published on September 10, 2025.
Keep an eye on advancements in text analytics, as systems like VIDEE continue to expand the horizons of what’s achievable, making powerful analytical tools accessible to all. The focus on usability for new analysts demonstrates a robust commitment to fostering a more inclusive data analysis environment.
In Summary
The developments encapsulated within VIDEE represent a monumental shift in text analytics. By leveraging human-agent collaboration and modern AI techniques, the system not only empowers novice analysts but also informs the future of intelligent text analytics. As we monitor this exciting evolution, the ability to effectively engage with textual data is poised to become more accessible, heralding a new era of insight generation.
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