Causal-CoG: A Causal-Effect Look at Context Generation for Boosting Multi-modal Language Models

While Multi-modal Language Models (\textit{MLMs}) demonstrate impressive multimodal ability, they still struggle on providing factual and precise responses for tasks like visual question answering (\textit{VQA}).In this paper, we address this challenge from the perspective of contextual information. We propose Causal Context Generation, \textbf{Causal-CoG}, which is a prompting strategy that engages contextual information to enhance precise VQA during inference. Specifically, we prompt MLMs to generate contexts, i.e, text description of an image, and engage the generated contexts for question answering. Moreover, we investigate the advantage of contexts on VQA from a causality perspective, introducing causality filtering to select samples for which contextual information is helpful. To show the effectiveness of Causal-CoG, we run extensive experiments on 10 multimodal benchmarks and show consistent improvements, \emph{e.g.}, +6.30\% on POPE, +13.69\% on Vizwiz and +6.43\% on VQAv2 compared to direct decoding, surpassing existing methods. We hope Casual-CoG inspires explorations of context knowledge in multimodal models, and serves as a plug-and-play strategy for MLM decoding.