The ability to learn from context with novel concepts, and deliver
appropriate responses are essential in human conversations. Despite current
Multimodal Large Language Models (MLLMs) and Large Language Models (LLMs) being
trained on mega-scale datasets, recognizing unseen images or understanding
novel concepts in a training-free manner remains a challenge. In-Context
Learning (ICL) explores training-free few-shot learning, where models are
encouraged to ``learn to learn" from limited tasks and generalize to unseen
tasks. In this work, we propose link-context learning (LCL), which emphasizes
"reasoning from cause and effect" to augment the learning capabilities of
MLLMs. LCL goes beyond traditional ICL by explicitly strengthening the causal
relationship between the support set and the query set. By providing
demonstrations with causal links, LCL guides the model to discern not only the
analogy but also the underlying causal associations between data points, which
empowers MLLMs to recognize unseen images and understand novel concepts more
effectively. To facilitate the evaluation of this novel approach, we introduce
the ISEKAI dataset, comprising exclusively of unseen generated image-label
pairs designed for link-context learning. Extensive experiments show that our
LCL-MLLM exhibits strong link-context learning capabilities to novel concepts
over vanilla MLLMs. Code and data will be released at
this https URL

基于多模式大型语言模型和大型语言模型，提出了一种新颖的上下文学习方法（In-Context Learning），通过加强因果关系，引导模型识别类比和数据点间的潜在因果关联，使模型能够识别未见过的图像和理解新概念更有效。