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arxiv: 2305.03726 · v2 · submitted 2023-05-05 · 💻 cs.CV · cs.CL

Recognition: 2 theorem links

· Lean Theorem

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

Bo Li , Yuanhan Zhang , Liangyu Chen , Jinghao Wang , Fanyi Pu , Joshua Adrian Cahyono , Jingkang Yang , Ziwei Liu
Authors on Pith no claims yet

Pith reviewed 2026-05-15 02:40 UTC · model grok-4.3

classification 💻 cs.CV cs.CL
keywords Otter modelMIMIC-IT datasetmulti-modal instruction tuningin-context learningFlamingo architecturevideo understandingvisual assistantsmulti-image reasoning
0
0 comments X

The pith

Otter improves multi-modal instruction following by training on in-context examples from both text and images or videos.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents Otter, a model that extends Flamingo by incorporating textual and visual in-context examples during instruction tuning. This setup uses the MIMIC-IT dataset of over three million multi-modal instruction-response pairs to train the model for general-purpose visual assistance. The central idea is that providing diverse in-context examples across images and videos helps the model converge faster and generalize to new tasks involving multiple images or dynamic video. If this holds, models could handle complex visual queries more reliably without relying solely on single-instruction training. The work targets the gap where prior models did not systematically use multi-modal contexts for better instruction adherence.

Core claim

Otter builds on the Flamingo Perceiver architecture and is instruction-tuned on the MIMIC-IT dataset, which supplies diverse in-context examples across text, multiple images, and video; this training substantially improves convergence speed and generalization on tasks that require understanding complex video content and multi-image scenarios.

What carries the argument

The MIMIC-IT dataset and its per-entry curation of in-context multi-modal examples, which the model uses during instruction tuning to process combined text, image, and video inputs.

If this is right

  • Otter processes instructions that combine text with multiple images or video clips without separate handling steps.
  • Training convergence accelerates when in-context examples accompany each instruction.
  • Performance rises on video understanding and multi-image reasoning benchmarks.
  • The model supports a broader range of real-world visual assistant queries than single-instruction baselines.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar in-context tuning could be applied to other base multi-modal architectures to test if the gains transfer.
  • Dataset curation that emphasizes scenario diversity may reduce the total volume of data needed for strong multi-modal performance.
  • Future work could explore whether the same approach scales to longer video sequences or more images per context.
  • This points toward instruction tuning that treats context as a first-class input rather than an optional add-on.

Load-bearing premise

The assumption that MIMIC-IT's diverse in-context examples produce real generalization gains instead of improvements limited to the dataset's own scenarios.

What would settle it

Measure whether a version of Otter trained without in-context examples matches or exceeds the full model's accuracy on a held-out set of multi-image and video instruction tasks drawn from sources outside MIMIC-IT.

read the original abstract

Recent advances in Large Multimodal Models (LMMs) have unveiled great potential as visual assistants. However, most existing works 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 the instruction following capability. To bridge this gap, we introduce the \textbf{Otter} model to leverage both textual and visual in-context examples for instruction tuning. Specifically, Otter builds upon Flamingo with Perceiver architecture, and has been instruction tuned for general purpose multi-modal assistant. Otter seamlessly processes multi-modal inputs, supporting modalities including text, multiple images, and dynamic video content. To support the training of Otter, we present the \textbf{MIMIC-IT} (\textbf{M}ult\textbf{I}-\textbf{M}odal \textbf{I}n-\textbf{C}ontext \textbf{I}nstruction \textbf{T}uning) dataset, which encompasses over 3 million multi-modal instruction-response pairs, including approximately 2.2 million unique instructions across a broad spectrum of images and videos. MIMIC-IT has been carefully curated to feature a diverse array of in-context examples for each entry. Comprehensive evaluations suggest that instruction tuning with these in-context examples substantially enhances model convergence and generalization capabilities. Notably, the extensive scenario coverage provided by the MIMIC-IT dataset empowers the Otter model to excel in tasks involving complex video and multi-image understanding.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces the Otter model, built on the Flamingo Perceiver architecture, and the MIMIC-IT dataset of over 3 million multi-modal instruction-response pairs featuring diverse in-context examples across images and videos. It claims that instruction tuning with these in-context examples substantially enhances convergence and generalization, enabling strong performance on complex video and multi-image tasks.

Significance. If the empirical claims hold with proper validation, the work would advance multi-modal in-context learning by releasing a large-scale dataset with broad scenario coverage and demonstrating a model that handles variable multi-image and video inputs. The dataset curation and architectural integration represent potentially useful resources for general-purpose visual assistants.

major comments (3)
  1. [Abstract] Abstract: The assertion that 'instruction tuning with these in-context examples substantially enhances model convergence and generalization capabilities' is unsupported by any quantitative metrics, baselines (e.g., Flamingo), error bars, or evaluation details, so the data-to-claim link cannot be assessed.
  2. [Experiments] Experiments section: No ablation isolates the contribution of in-context structure (multiple images/videos per instruction) versus equivalent data volume or scenario coverage in single-instruction format, leaving dataset scale as a plausible alternative explanation for any gains.
  3. [Model Architecture] Model Architecture section: Details are missing on how variable-length visual sequences from multiple images and dynamic videos are tokenized, embedded, and attended within the existing Flamingo Perceiver without hidden limitations or modifications.
minor comments (2)
  1. [Experiments] Add explicit comparison tables against Flamingo and other LMM baselines with standard metrics (e.g., accuracy, CIDEr) and statistical significance tests.
  2. [Method] Clarify notation for in-context example formatting and provide pseudocode for how multi-modal sequences are constructed during training.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below, providing clarifications from the manuscript and indicating where revisions will be made to strengthen the presentation of results and technical details.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The assertion that 'instruction tuning with these in-context examples substantially enhances model convergence and generalization capabilities' is unsupported by any quantitative metrics, baselines (e.g., Flamingo), error bars, or evaluation details, so the data-to-claim link cannot be assessed.

    Authors: We appreciate this observation. The manuscript's Section 4 reports quantitative evaluations across multiple benchmarks, including direct comparisons to the base Flamingo model, with performance metrics on video and multi-image tasks and error bars in the tables. The abstract summarizes these findings at a high level. To make the link between data and claims explicit, we will revise the abstract to include specific quantitative improvements (e.g., accuracy gains on key tasks) supported by the experimental results. revision: yes

  2. Referee: [Experiments] Experiments section: No ablation isolates the contribution of in-context structure (multiple images/videos per instruction) versus equivalent data volume or scenario coverage in single-instruction format, leaving dataset scale as a plausible alternative explanation for any gains.

    Authors: This is a fair critique. The current experiments demonstrate gains from instruction tuning on MIMIC-IT but do not include an ablation that holds total data volume fixed while varying only the in-context (multi-image/video) structure versus single-instruction format. Creating a matched single-instruction control set at the same scale would require substantial additional curation. We will add a dedicated paragraph in the Experiments section acknowledging this limitation, discussing why the in-context design is central to the dataset, and noting it as an avenue for future work. revision: partial

  3. Referee: [Model Architecture] Model Architecture section: Details are missing on how variable-length visual sequences from multiple images and dynamic videos are tokenized, embedded, and attended within the existing Flamingo Perceiver without hidden limitations or modifications.

    Authors: We thank the referee for noting this gap. Otter uses the Flamingo Perceiver architecture without modifications to its core resampler or attention layers. Multiple images are encoded independently by the vision encoder and their visual tokens are concatenated into a single sequence; videos are processed by uniformly sampling frames and treating the resulting sequence as additional visual tokens. The Perceiver resampler then maps the variable-length visual sequence to a fixed set of latents for cross-attention with the language model. We will expand the Model Architecture section with a new subsection that explicitly describes the input preprocessing, tokenization, embedding, and attention flow for these variable-length cases. revision: yes

Circularity Check

0 steps flagged

No circularity: claims rest on new dataset and standard training

full rationale

The paper introduces Otter by extending the external Flamingo Perceiver architecture and trains it on the newly curated MIMIC-IT dataset containing multi-modal in-context examples. The central claim—that in-context instruction tuning enhances convergence and generalization—is presented as an empirical outcome of this training and subsequent evaluations, with no equations, fitted parameters, or self-citations that reduce the result to a definitional loop or renamed input. No self-definitional steps, fitted-input predictions, or load-bearing self-citations appear in the derivation chain; the work is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central claim depends on the effectiveness of the newly curated MIMIC-IT dataset and the compatibility of the Flamingo base model with added in-context visual examples; these rest on unstated assumptions about data diversity and architecture suitability.

free parameters (2)
  • Number and selection of in-context examples per training instance
    Chosen during dataset curation and training but not quantified in the abstract.
  • Instruction tuning hyperparameters
    Standard training settings required for convergence but unspecified.
axioms (1)
  • domain assumption The Perceiver architecture in the Flamingo base model enables seamless handling of text, multiple images, and dynamic video inputs.
    Invoked when stating that Otter processes multi-modal inputs without additional architectural changes.

pith-pipeline@v0.9.0 · 5579 in / 1213 out tokens · 31120 ms · 2026-05-15T02:40:09.150748+00:00 · methodology

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