GlitchBench: Can large multimodal models detect video game glitches?
arxiv(2023)
摘要
Large multimodal models (LMMs) have evolved from large language models (LLMs)
to integrate multiple input modalities, such as visual inputs. This integration
augments the capacity of LLMs for tasks requiring visual comprehension and
reasoning. However, the extent and limitations of their enhanced abilities are
not fully understood, especially when it comes to real-world tasks. To address
this gap, we introduce GlitchBench, a novel benchmark derived from video game
quality assurance tasks, to test and evaluate the reasoning capabilities of
LMMs. Our benchmark is curated from a variety of unusual and glitched scenarios
from video games and aims to challenge both the visual and linguistic reasoning
powers of LMMs in detecting and interpreting out-of-the-ordinary events. We
evaluate multiple state-of-the-art LMMs, and we show that GlitchBench presents
a new challenge for these models. Code and data are available at:
https://glitchbench.github.io/
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