VL-ICL Bench: The Devil in the Details of Multimodal In-Context Learning

Large language models (LLMs) famously exhibit emergent in-context learning (ICL) -the ability to rapidly adapt to new tasks using few-shot examples provided as a prompt, without updating the model's weights. Built on top of LLMs, vision large language models (VLLMs) have advanced significantly in areas such as recognition, visual question answering (VQA), reasoning, and grounding. However, investigations into multimodal ICL have predominantly focused on few-shot VQA and image captioning, which we will show neither exploit the strengths of ICL, nor test its limitations. The broader capabilities and limitations of multimodal ICL remain under-explored. In this study, we introduce a comprehensive benchmark for multimodal in-context learning. Our VL-ICL Bench encompasses a broad spectrum of tasks that involve both images and text as inputs and outputs, and different types of challenges, from perception to reasoning and long context length. We evaluate the abilities of state-of-theart VLLMs on this benchmark suite, revealing their diverse strengths and weaknesses, and showing that even the most advanced models, such as GPT-4, find the tasks challenging. By highlighting a range of new ICL tasks, and the associated strengths and limitations of existing models, we hope that our dataset will inspire future work on enhancing the in-context learning capabilities of VLLMs, as well as inspire new applications that leverage VLLM ICL. Project page: https://ys-zong.github.io/VL-ICL/ .