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Related Experiment Video

Updated: Sep 16, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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Do Language Model Agents Align With Humans in Rating Visualizations? An Empirical Study.

Zekai Shao, Yi Shan, Yixuan He

    IEEE Computer Graphics and Applications
    |July 9, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Large language models (LLMs) can simulate human ratings for visualization design tasks, but only when guided by expert confidence. These AI agents complement, but do not replace, traditional user studies.

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    Area of Science:

    • Human-computer interaction
    • Artificial intelligence
    • Data visualization

    Background:

    • Large language models (LLMs) demonstrate potential in interpreting visual data and design principles.
    • The capacity of LLMs to accurately predict human feedback on visualization design remains largely unexplored.

    Purpose of the Study:

    • To evaluate the alignment between LLM-based agent ratings and human judgments in visualization tasks.
    • To investigate the factors influencing the accuracy of LLM predictions for human feedback.
    • To explore the utility of LLM agents in rapid prototype evaluation.

    Main Methods:

    • Three studies were conducted: replication of a human-subject study, simulation of six prior studies using LLM agents, and testing of enhancement techniques.
    • LLM agents were employed to provide ratings on visualization tasks, and their performance was compared against human subject data and expert confidence levels.
    • Techniques such as input preprocessing and knowledge injection were tested to assess their impact on agent robustness and bias.

    Main Results:

    • LLM agents showed promising performance in mimicking human-like reasoning and ratings in a replicated study.
    • Agent-human alignment in simulated studies correlated positively with experts' pre-experiment confidence.
    • Enhancement techniques revealed limitations in robustness and potential for bias in LLM agents.

    Conclusions:

    • LLM-based agents can effectively simulate human ratings for visualization tasks when guided by high-confidence expert hypotheses.
    • LLM agents show potential as a complementary tool for rapid prototype evaluation in visualization design.
    • LLM simulations are valuable but cannot substitute for comprehensive user studies.