Multimodal Instruction Tuning

Transforms diverse tasks into a unified sequence-to-sequence format. An instruction like 'Describe the anomaly in this medical scan' is structured as a single prompt containing interleaved text tokens and visual patch embeddings. This allows a single model to handle Visual Question Answering (VQA), captioning, and grounding without task-specific heads.

Relies on a high-quality dataset of multimodal instruction-response pairs. Data often originates from:

• Transforming existing annotated datasets (e.g., COCO captions) into an instructional format.
• Using a powerful Vision-Language Model (VLM) to generate diverse question-answer pairs from images.
• Human annotators crafting complex reasoning prompts that require visual grounding and multi-step logic.

Often employs Low-Rank Adaptation (LoRA) or adapter layers rather than full fine-tuning. This freezes the pre-trained weights of the modality encoders and the Multimodal Transformer while training a small number of additional parameters. This preserves the model's broad pre-trained knowledge while efficiently aligning it to the instruction-following distribution.

The tuning dataset includes examples that elicit step-by-step reasoning grounded in visual evidence. A model is trained to output an intermediate textual rationale that references specific image regions before delivering the final answer. This significantly improves performance on complex tasks like Chart Question Answering and scientific diagram interpretation.

The training protocol includes negative instructions where the model must learn to refuse or indicate an inability to comply. Examples include requests for non-existent objects in an image or queries requiring modalities not present. This is a critical mechanism for Multimodal Hallucination Mitigation, teaching the model to ground its responses strictly in the provided input.