Zero Shot And Few Shot Multimodal Instruction Following

via “few-shot learning via in-context examples”

text-generation model by undefined. 92,07,977 downloads.

Unique: Leverages instruction-tuning to recognize and generalize from in-context examples without fine-tuning, enabling task adaptation through prompt engineering alone — a capability that emerges from training on diverse instruction-following datasets rather than explicit few-shot learning objectives

vs others: More practical than zero-shot for complex tasks; faster iteration than fine-tuning but less accurate than task-specific fine-tuned models

via “few-shot prompt adaptation via in-context learning”

text-generation model by undefined. 61,45,130 downloads.

Unique: Instruction-tuning enables the model to reliably recognize and follow patterns from in-context examples without explicit task specification — the model learns to infer task intent from demonstrations rather than requiring explicit instructions

vs others: More flexible than fixed-task models but less reliable than fine-tuned models; faster iteration than fine-tuning but requires more careful prompt engineering than larger models with stronger in-context learning

via “zero-shot and few-shot multimodal instruction following”

* ⭐ 03/2023: [PaLM-E: An Embodied Multimodal Language Model (PaLM-E)](https://arxiv.org/abs/2303.03378)

Unique: Trained on diverse multimodal tasks at scale, enabling generalization to arbitrary new instructions without gradient updates, using in-context learning patterns learned during pretraining rather than task-specific fine-tuning

vs others: More flexible than task-specific fine-tuned models because it follows natural language instructions; more sample-efficient than training new models for each task

via “few-shot in-context learning with examples”

Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases. It has demonstrated strong...

Unique: Llama 3 8B's instruction-tuning includes meta-learning patterns that improve few-shot generalization — the model was trained to recognize and apply patterns from examples more effectively than base models. The training data includes diverse few-shot scenarios, improving the model's ability to infer task intent from limited examples.

vs others: Achieves few-shot performance comparable to GPT-3.5 with significantly lower API costs; more consistent few-shot learning than Mistral 7B due to superior instruction-tuning on example-based tasks.

via “multimodal instruction following with complex prompts”

Qwen3-VL-32B-Instruct is a large-scale multimodal vision-language model designed for high-precision understanding and reasoning across text, images, and video. With 32 billion parameters, it combines deep visual perception with advanced text...

Unique: Instruction-tuned architecture enables reliable parsing and execution of complex multimodal prompts with explicit format and reasoning constraints, maintaining consistency across diverse task specifications

vs others: More reliable instruction-following than base vision models; supports more complex prompt structures than simpler VLMs while remaining more cost-effective than fine-tuned specialized models

via “multimodal-few-shot-and-zero-shot-learning”


Unique: Systematically leverages cross-modal alignment to enable more effective few-shot learning, with concrete strategies for using textual descriptions to guide visual learning — a multimodal-specific advantage absent from single-modality few-shot learning

vs others: Unique focus on how multimodal information (visual + textual) enables more effective few-shot learning compared to single-modality meta-learning; integrates prompt-based learning with metric learning approaches