What Makes Multimodal In-Context Learning Work?
| Autor: | Baldassini, Folco Bertini, Shukor, Mustafa, Cord, Matthieu, Soulier, Laure, Piwowarski, Benjamin |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Large Language Models have demonstrated remarkable performance across various tasks, exhibiting the capacity to swiftly acquire new skills, such as through In-Context Learning (ICL) with minimal demonstration examples. In this work, we present a comprehensive framework for investigating Multimodal ICL (M-ICL) in the context of Large Multimodal Models. We consider the best open-source multimodal models (e.g., IDEFICS, OpenFlamingo) and a wide range of multimodal tasks. Our study unveils several noteworthy findings: (1) M-ICL primarily relies on text-driven mechanisms, showing little to no influence from the image modality. (2) When used with advanced-ICL strategy (like RICES), M-ICL is not better than a simple strategy based on majority voting over context examples. Moreover, we identify several biases and limitations of M-ICL that warrant consideration prior to deployment. Code available at https://gitlab.com/folbaeni/multimodal-icl Comment: 20 pages, 16 figures. Accepted to CVPR 2024 Workshop on Prompting in Vision. Project page: https://folbaeni.gitlab.io/multimodal-icl |
| Databáze: | arXiv |
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