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arXiv Computation and Language By David Melhart, Matthew Barthet, Georgios N. Yannakakis

Can Large Language Models Capture Video Game Engagement?

Read the original on arXiv Computation and Language →

The paper investigates whether out‑of‑the‑box pretrained Large Language Models (LLMs) can detect human affect by predicting continuous engagement annotations from video game footage. Using 80 minutes of annotated first‑person shooter gameplay from the GameVibe corpus, the authors run over 4,800 experiments to assess how LLM architecture, size, input modality, prompting strategy, and ground‑truth processing affect performance. Results show that while LLMs can outperform traditional machine‑learning baselines, they generally lag behind human‑provided continuous experience annotations, and the study explores reasons for performance variability and outlines directions for future automated emotion labeling.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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