Otter: A Multi-Modal Model With In-Context Instruction Tuning

Analysis

Why This Paper Matters

Large Multimodal Models (LMMs) have shown promise as visual assistants, but most existing approaches focus on responding to individual instructions or using previous dialogues for contextual understanding. There is little discussion on employing both images and text as in-context examples to enhance instruction following. This paper addresses that gap by introducing Otter, a model that leverages both textual and visual in-context examples for instruction tuning.

The significance lies in the potential to improve how models generalize from examples that include visual context, moving beyond text-only in-context learning. By releasing the MIMIC-IT dataset with over 3 million instruction-response pairs, the authors provide a substantial resource for training and evaluating multi-modal assistants. This work is positioned to influence future research on general-purpose multi-modal models that can handle complex tasks involving multiple images and video.

Technical Contributions

• Otter Model: Builds upon the Flamingo architecture with Perceiver, instruction-tuned for general-purpose multi-modal assistance. It seamlessly processes text, multiple images, and dynamic video content.
• MIMIC-IT Dataset: A large-scale dataset comprising over 3 million multi-modal instruction-response pairs, including approximately 2.2 million unique instructions across diverse images and videos. Each entry features carefully curated in-context examples.
• In-Context Instruction Tuning: Explicitly incorporates both textual and visual in-context examples during tuning, a departure from prior work that typically uses individual instructions or dialogue history.
• Evaluation Focus: Comprehensive evaluations demonstrate that in-context examples enhance convergence and generalization, and that scenario coverage enables strong performance on complex video and multi-image understanding.

Results

The abstract reports that instruction tuning with in-context examples substantially enhances model convergence and generalization capabilities. Notably, the extensive scenario coverage provided by MIMIC-IT empowers Otter to excel in tasks involving complex video and multi-image understanding. However, specific quantitative metrics or comparisons with other models are not provided in the abstract, so concrete performance numbers cannot be cited here.

Significance

This work contributes both a model and a large-scale dataset to the multi-modal instruction tuning landscape. By emphasizing visual in-context examples, it opens avenues for improving instruction following in scenarios where visual context is crucial. The MIMIC-IT dataset, with its scale and diversity, could serve as a benchmark and training resource for the community. Ultimately, Otter's approach may inform the development of more capable and general-purpose multi-modal assistants, though further empirical validation will determine its full impact.