What If the TV Was Off? Examining Counterfactual Reasoning Abilities of Multi-modal Language Models

Letian Zhang1, Xiaotong Zhai2, Zhongkai Zhao3,6, Yongshuo Zong4, Xin Wen5,6, Bingchen Zhao4,6
1Tongji University, 2University of Warwick, 3National University of Singapore, 4University of Edinburgh, 5The University of Hong Kong, 6LunarAI

We observe that state-of-the-art models all exhibited significant performance drops on the counterfactual questions.


Counterfactual reasoning, a fundamental aspect of human cognition, involves contemplating alternatives to established facts or past events, significantly enhancing our abilities in planning and decision-making. In light of the advancements in current multi-modal large language models, we explore their effectiveness in counterfactual reasoning. To facilitate this investigation, we introduce a novel dataset, C-VQA, specifically designed to test the counterfactual reasoning capabilities of modern multi-modal large language models. This dataset is constructed by infusing original questions with counterfactual presuppositions, spanning various types such as numerical and boolean queries. It encompasses a mix of real and synthetic data, representing a wide range of difficulty levels. Our thorough evaluations of contemporary vision-language models using this dataset have revealed substantial performance drops, with some models showing up to a 40% decrease, highlighting a significant gap between current models and human-like vision reasoning capabilities. We hope our dataset will serve as a vital benchmark for evaluating the counterfactual reasoning capabilities of models.


      author    = {Zhang, Letian and Zhai, Xiaotong and Zhao, Zhongkai and Zong, Yongshuo and Wen, Xin and Zhao, Bingchen},
      title     = {What If the TV Was Off? Examining Counterfactual Reasoning Abilities of Multi-modal Language Models},
      journal   = {arXiv:2310.06627},
      year      = {2023}