Social media has become a ubiquitous tool for connecting with others, staying
updated with news, expressing opinions, and finding entertainment. However,
understanding the intention behind social media posts remains challenging due
to the implicitness of intentions in social media posts, the need for
cross-modality understanding of both text and images, and the presence of noisy
information such as hashtags, misspelled words, and complicated abbreviations.
To address these challenges, we present MIKO, a Multimodal Intention Kowledge
DistillatiOn framework that collaboratively leverages a Large Language Model
(LLM) and a Multimodal Large Language Model (MLLM) to uncover users'
intentions. Specifically, we use an MLLM to interpret the image and an LLM to
extract key information from the text and finally instruct the LLM again to
generate intentions. By applying MIKO to publicly available social media
datasets, we construct an intention knowledge base featuring 1,372K intentions
rooted in 137,287 posts. We conduct a two-stage annotation to verify the
quality of the generated knowledge and benchmark the performance of widely used
LLMs for intention generation. We further apply MIKO to a sarcasm detection
dataset and distill a student model to demonstrate the downstream benefits of
applying intention knowledge.

为了理解社交媒体帖子中的意图，我们提出了 MIKO，一种多模态意图知识提取框架，通过协同使用大型语言模型和多模态大型语言模型来揭示用户的意图。应用 MIKO 到公开的社交媒体数据集，我们构建了一个包含 137,287 条帖子中 1,372,000 个意图的意图知识库，并通过两阶段注释验证了生成的知识的质量，并对常用的大型语言模型进行意图生成的性能基准测试。我们进一步将 MIKO 应用于讽刺检测数据集，并提取出一个学生模型来展示应用意图知识的下游效益。