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LLMs tested for video game engagement prediction, fall short of human performance

By PulseAugur Editorial · [1 sources] ·

A new arXiv paper investigates the capabilities of large language models (LLMs) in predicting human affect and engagement within video games. Researchers tested various LLMs on the GameVibe corpus, which contains 80 minutes of annotated footage from 20 first-person shooter games. The study found that while LLMs can outperform traditional machine learning models, they generally fall short of human-level continuous experience annotations. AI

IMPACT Investigates LLM capabilities in multimodal understanding and affect detection, potentially informing future AI applications in gaming and human-computer interaction.

RANK_REASON Academic paper published on arXiv detailing research findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs tested for video game engagement prediction, fall short of human performance

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Academic paper published on arXiv detailing research findings. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 (CA) · David Melhart, Matthew Barthet, Georgios N. Yannakakis ·

    Can Large Language Models Capture Video Game Engagement?

    arXiv:2502.04379v3 Announce Type: replace-cross Abstract: Can out-of-the-box pretrained Large Language Models (LLMs) detect human affect successfully when observing a video? To address this question, for the first time, we evaluate comprehensively the capacity of popular LLMs for…

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