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+  <title type="html">ArXiv Query: search_query=&amp;id_list=2306.05425&amp;start=0&amp;max_results=1</title>
+  <id>http://arxiv.org/api/UKr4sG8yTUNI8GqLUahlRDrZ5vk</id>
+  <updated>2023-06-09T00:00:00-04:00</updated>
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+  <entry>
+    <id>http://arxiv.org/abs/2306.05425v1</id>
+    <updated>2023-06-08T17:59:56Z</updated>
+    <published>2023-06-08T17:59:56Z</published>
+    <title>MIMIC-IT: Multi-Modal In-Context Instruction Tuning</title>
+    <summary>  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.
+</summary>
+    <author>
+      <name>Bo Li</name>
+    </author>
+    <author>
+      <name>Yuanhan Zhang</name>
+    </author>
+    <author>
+      <name>Liangyu Chen</name>
+    </author>
+    <author>
+      <name>Jinghao Wang</name>
+    </author>
+    <author>
+      <name>Fanyi Pu</name>
+    </author>
+    <author>
+      <name>Jingkang Yang</name>
+    </author>
+    <author>
+      <name>Chunyuan Li</name>
+    </author>
+    <author>
+      <name>Ziwei Liu</name>
+    </author>
+    <arxiv:comment xmlns:arxiv="http://arxiv.org/schemas/atom">Project page: https://otter-ntu.github.io/ Dataset &amp; code:
+  https://github.com/Luodian/otter Initial release, work in progress</arxiv:comment>
+    <link href="http://arxiv.org/abs/2306.05425v1" rel="alternate" type="text/html"/>
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