Visual Instruction Tuning Task

A Visual Instruction Tuning Task is a instruction fine-tuning task that enhances Large Language Models with visual capabilities through instruction-based learning.

• Context:
  • It can (typically) involve training a pre-trained language model on a dataset containing instructions or prompts paired with visual data, aiming to improve the model's performance on multimodal tasks.
  • It can (often) leverage datasets with paired image-text data or structured tasks that require understanding and responding to visual content.
  • It can enhance models' abilities in Visual Question Answering, Image Captioning, and other tasks requiring joint understanding of text and imagery.
  • ...
• Example(s):
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• Counter-Example(s):
  • Standard language model fine-tuning using only text-based tasks.
  • Direct training on visual tasks without using language-modeling principles or instructions.
• See: Language Model Fine-Tuning, Multimodal Learning, Instruction-Based Learning.

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References

2024

• (McKinzie et al., 2024) ⇒ Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah et al. (2024). “MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training.” arXiv preprint arXiv:2403.09611
  • QUOTE: "Recent research has increasingly focused on visual instruction tuning on top of the pre-trained LLM. Prominent examples include LLaVA(-1.5/NeXT), MiniGPT-4, mPLUG-Owl(-2/Doc), Otter, InstructBLIP, Honeybee, SPHINX(-X) to name a few. There is also a rich body of literature on constructing instruction-tuning data enabling MLLMs for referring and grounding, image generation, and editing.

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