GlitchBench: Can large multimodal models detect video game glitches?
This work addresses the need for better evaluation of LMMs in practical scenarios, specifically for video game quality assurance, but it is incremental as it focuses on benchmarking rather than model improvement.
The authors tackled the problem of understanding the capabilities of large multimodal models (LMMs) in real-world tasks by introducing GlitchBench, a benchmark for detecting video game glitches, and found that it presents a new challenge for state-of-the-art LMMs.
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/