metadata

dataset_info:
  features:
    - name: image
      dtype: image
    - name: id
      dtype: string
    - name: reddit
      dtype: string
    - name: glitch-type
      dtype: string
    - name: game
      dtype: string
    - name: source
      dtype: string
    - name: description
      dtype: string
    - name: __index_level_0__
      dtype: int64
  splits:
    - name: validation
      num_bytes: 686309290
      num_examples: 607
  download_size: 686303027
  dataset_size: 686309290
license: mit
task_categories:
  - image-to-text
language:
  - en
tags:
  - Video Game
  - Glitch
pretty_name: GlitchBench
size_categories:
  - n<1K

GlitchBench

This repository contains the dataset for the paper GlitchBench: Can large multimodal models detect video game glitches?

by Mohammad Reza Taesiri, Tianjun Feng, Anh Nguyen, and Cor-Paul Bezemer

(CVPR 2024)

Abstract

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 in tasks requiring visual comprehension and reasoning. However, the extent and limitations of their enhanced abilities are not fully understood. To address this gap, we introduce GlitchBench, a novel benchmark designed to test and evaluate the common-sense reasoning and visual recognition capabilities of large multimodal models. Our dataset is curated from a variety of unusual, infrequent, and glitched scenarios from video game content and aims to challenge both the visual and linguistic reasoning powers of LMMs in detecting and interpreting out-of-the-ordinary events and scene composition.