Multimodal Few Shot And Zero Shot Learning

via “zero-shot image classification via natural language descriptions”

OpenAI's vision-language model for zero-shot classification.

Unique: Uses contrastive pre-training on 400M image-text pairs from the internet to learn a shared embedding space where visual and linguistic concepts align, enabling zero-shot transfer without task-specific fine-tuning. The dual-encoder design (separate image and text pathways) allows flexible composition of new classes at inference time by encoding arbitrary text descriptions.

vs others: Outperforms traditional supervised classifiers on novel categories and requires no labeled training data, whereas models like ResNet-50 require thousands of labeled examples per class and cannot generalize to unseen categories.

via “zero-shot and few-shot task generalization through in-context learning”

01.AI's bilingual 34B model with 200K context option.

Unique: Bilingual in-context learning enables cross-lingual few-shot adaptation — users can provide examples in English and apply the learned pattern to Chinese inputs or vice versa

vs others: Few-shot performance is likely comparable to Llama 2 34B but inferior to GPT-3.5 and Claude, which demonstrate superior in-context learning and few-shot generalization

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

text-generation model by undefined. 95,66,721 downloads.

Unique: Few-shot learning emerges from transformer attention mechanisms learning patterns from in-context examples without explicit meta-learning modules; enables rapid task adaptation by processing examples as part of input context, avoiding fine-tuning overhead

vs others: Faster task adaptation than fine-tuning-based approaches; comparable to GPT-3.5 on few-shot performance but with local control; outperforms Mistral-7B on instruction-following few-shot tasks due to explicit instruction tuning

via “zero-shot and few-shot generalization via task diversity”

Google's 1,836-task instruction mixture for broad generalization.

Unique: Explicitly designs task diversity to maximize zero-shot and few-shot generalization rather than optimizing for in-distribution performance, using 1,836 tasks to create a broad instruction-following capability that transfers to unseen tasks. This is a deliberate design choice reflected in published Flan-T5 and Flan-PaLM results.

vs others: Dramatically improves zero-shot and few-shot performance compared to non-instruction-tuned models and single-task fine-tuned models, with published results showing 10-30% improvements on held-out benchmarks, making it substantially more effective for rapid task adaptation than alternatives.

via “few-shot and zero-shot task adaptation via in-context learning”

text-generation model by undefined. 1,13,49,614 downloads.

Unique: DeepSeek-V3.2 was trained with explicit in-context learning objectives, using diverse task examples during training to improve few-shot adaptation. The sparse MoE architecture allows task-specific experts to activate based on example patterns, improving few-shot performance without explicit task-specific fine-tuning.

vs others: Achieves 5-10% higher few-shot accuracy than Llama-2-70B on SuperGLUE and XTREME benchmarks due to specialized in-context learning training, while maintaining lower inference cost due to sparse activation

via “zero-shot and few-shot learning via embedding similarity”

fill-mask model by undefined. 5,92,18,905 downloads.

Unique: Leverages pre-trained bidirectional context to generate semantically rich embeddings that generalize to unseen classes without task-specific fine-tuning; enables rapid prototyping and dynamic category addition

vs others: More practical than true zero-shot methods (e.g., natural language inference) because it uses simple cosine similarity, and more data-efficient than supervised fine-tuning for low-resource scenarios

via “few-shot in-context learning for task adaptation”

text-generation model by undefined. 1,00,18,533 downloads.

Unique: Qwen3-8B's instruction-tuning and reasoning capabilities enable strong few-shot performance across diverse tasks without task-specific fine-tuning. The model's 8K context window provides sufficient space for examples + input for most practical tasks.

vs others: Achieves comparable few-shot accuracy to larger models (GPT-3.5, Llama 70B) while being 8-10x smaller, making it practical for local deployment with few-shot capabilities

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 learning through in-context examples”

text-generation model by undefined. 51,86,179 downloads.

Unique: Qwen3-1.7B demonstrates in-context learning capability through instruction-tuning, enabling few-shot adaptation without fine-tuning. The model's small size makes few-shot learning less reliable than larger models but still practical for many tasks.

vs others: More flexible than fine-tuning-only approaches; weaker in-context learning than GPT-3.5 or Llama-2-7B but sufficient for many production tasks; no fine-tuning overhead compared to task-specific models.

via “zero-shot-and-few-shot-adaptation-via-prompt-engineering”

text-classification model by undefined. 34,16,580 downloads.

Unique: Distilled architecture retains rich semantic representations (768-dim hidden states) suitable for few-shot learning while reducing inference latency, enabling rapid task adaptation without full fine-tuning. Hidden states from all 6 layers can be extracted and combined for task-specific feature engineering.

vs others: More efficient for few-shot adaptation than training from scratch, but less flexible than larger models (RoBERTa, GPT-3) for highly novel tasks requiring greater representational capacity.

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

text-generation model by undefined. 36,85,809 downloads.

Unique: Achieves few-shot adaptation through attention-based pattern matching on in-context examples without requiring model modification or external retrieval systems. Instruction-tuning enables the model to recognize and generalize from diverse example formats (code, reasoning, structured data) within a single forward pass.

vs others: More effective at few-shot learning than base Llama-2-3B due to instruction-tuning; comparable to GPT-3.5-Turbo on few-shot tasks while remaining fully open-source and deployable locally, enabling private few-shot experimentation without API dependencies.

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 “pretrained-knowledge-transfer-for-zero-shot-tasks”

fill-mask model by undefined. 43,77,886 downloads.

Unique: Transfers 110M parameters of pretrained linguistic knowledge learned from 3.3B token corpus to zero-shot tasks by leveraging learned embeddings and attention patterns, without task-specific fine-tuning — enabling rapid prototyping but with inherent performance ceiling due to mismatch between pretraining and downstream objectives

vs others: Faster and cheaper than fine-tuning (no labeled data required), but significantly lower performance than fine-tuned models; larger models (GPT-3) show better zero-shot performance through prompt engineering, but require API access and higher inference costs

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

22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.

Unique: Isolates few-shot learning as a distinct technique with explicit notebooks showing example selection strategies, formatting patterns, and empirical comparison of few-shot vs zero-shot performance. Uses real API calls to demonstrate token cost vs accuracy tradeoffs rather than theoretical discussion.

vs others: More systematic than ad-hoc few-shot prompting because it teaches example curation principles and provides measurable comparisons, whereas most guides treat few-shot as an afterthought to zero-shot.

via “developmentally efficient learning through zero-shot world models”

Zero-shot World Models Are Developmentally Efficient Learners [R]

Unique: Utilizes a hierarchical developmental framework that allows for efficient learning and generalization, unlike traditional models that require extensive datasets.

vs others: More efficient in learning from fewer examples compared to conventional supervised models, making it suitable for environments with limited data.

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

Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — delivering near-31B quality at...

Unique: Few-shot learning emerges from instruction-tuning and large-scale pretraining, not explicit meta-learning architecture. The model learns to recognize and generalize patterns from examples through standard next-token prediction, making it flexible but less reliable than explicit meta-learning approaches.

vs others: Provides comparable few-shot performance to GPT-4 for most tasks while being 3x cheaper per token, making few-shot adaptation economical for production systems that can tolerate slightly lower accuracy.

via “zero-shot image classification via text prompts”

Open reproduction of consastive language-image pretraining (CLIP) and related.

Unique: Implements zero-shot classification by leveraging the natural language understanding of CLIP's text encoder, allowing arbitrary class definitions via prompts rather than fixed label vocabularies, with support for hierarchical or descriptive class names that improve accuracy over simple category tokens

vs others: More flexible than traditional supervised classifiers because it adapts to new classes without retraining, but less accurate than fine-tuned models on specific domains due to reliance on pretraining knowledge

via “few-shot learning with in-context example optimization”

Gemini Flash 2.0 offers a significantly faster time to first token (TTFT) compared to [Gemini Flash 1.5](/google/gemini-flash-1.5), while maintaining quality on par with larger models like [Gemini Pro 1.5](/google/gemini-pro-1.5). It...

Unique: Gemini 2.0 Flash uses dynamic example weighting based on semantic similarity to the query, whereas most competitors treat all examples equally; this improves few-shot accuracy by 10-15% on diverse tasks.

vs others: Achieves comparable few-shot performance to GPT-4 with 50% fewer examples needed, making it more efficient for rapid prototyping and adaptation.

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 “zero-shot image generation with competitive benchmark performance”

* ⏫ 07/2023: [Meta-Transformer: A Unified Framework for Multimodal Learning (Meta-Transformer)](https://arxiv.org/abs/2307.10802)

Unique: Achieves competitive zero-shot image generation (FID 4.88) through unified autoregressive architecture with retrieval augmentation, rather than specialized diffusion models or task-specific fine-tuning, demonstrating that token-based approaches can match diffusion-based quality

vs others: More parameter-efficient than maintaining separate specialized text-to-image models; retrieval augmentation enables zero-shot performance without COCO-specific training, whereas most competing models require task-specific fine-tuning