Papers › MIMIC-IT: Multi-Modal In-Context Instruction Tuning
MIMIC-IT: Multi-Modal In-Context Instruction Tuning
Bo Li, Yuanhan Zhang, Liangyu Chen, Jinghao Wang, Fanyi Pu, Jingkang Yang, Chunyuan Li, Ziwei Liu
High-quality instructions and responses are essential for the zero-shot performance of large language models on interactive natural language tasks. For interactive vision-language tasks involving intricate visual scenes, a large quantity of diverse and creative instruction-response pairs should be imperative to tune vision-language models (VLMs). Nevertheless, the current availability of vision-language instruction-response pairs in terms of quantity, diversity, and creativity remains limited, posing challenges to the generalization of interactive VLMs. Here we present MultI-Modal In-Context Instruction Tuning (MIMIC-IT), a dataset comprising 2.8 million multimodal instruction-response pairs, with 2.2 million unique instructions derived from images and videos. Each pair is accompanied by multi-modal in-context information, forming conversational contexts aimed at empowering VLMs in perception, reasoning, and planning. The instruction-response collection process, dubbed as Syphus, is scaled using an automatic annotation pipeline that combines human expertise with GPT's capabilities. Using the MIMIC-IT dataset, we train a large VLM named Otter. Based on extensive evaluations conducted on vision-language benchmarks, it has been observed that Otter demonstrates remarkable proficiency in multi-modal perception, reasoning, and in-context learning. Human evaluation reveals it effectively aligns with the user's intentions. We release the MIMIC-IT dataset, instruction-response collection pipeline, benchmarks, and the Otter model.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Visual Question Answering | MM-Vet | Otter-9B (MPT-7B) | GPT-4 score | 24.7±0.3 | #222 of 231 | Archive leaderboard | report |
| Visual Question Answering | MM-Vet | Otter-9B (MPT-7B) | Params | 9B | #222 of 231 | Archive leaderboard | report |
| Visual Question Answering | MM-Vet | Otter-9B (LLaMA) | GPT-4 score | 24.6±0.2 | #223 of 231 | Archive leaderboard | report |
| Visual Question Answering | MM-Vet | Otter-9B (LLaMA) | Params | 9B | #223 of 231 | Archive leaderboard | report |
| Visual Question Answering | MM-Vet v2 | Otter-9B | GPT-4 score | 23.2±0.1 | #23 of 24 | Archive leaderboard | report |
| Visual Question Answering | MM-Vet v2 | Otter-9B | Params | 9B | #23 of 24 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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