Few Shot Learning With Extended In Context Examples

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

GPT-5.2 is the latest frontier-grade model in the GPT-5 series, offering stronger agentic and long context perfomance compared to GPT-5.1. It uses adaptive reasoning to allocate computation dynamically, responding quickly...

Unique: Leverages extended context window to accommodate multiple examples while maintaining reasoning quality, enabling more reliable few-shot learning than shorter-context models

vs others: More effective few-shot learning than GPT-4 due to longer context and improved reasoning, reducing need for fine-tuning compared to smaller models

via “few-shot in-context learning with example-based adaptation”

Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for...

Unique: Achieves few-shot adaptation through standard transformer attention over full context, with no special few-shot modules. The model learns to identify and apply patterns from examples via learned attention patterns during pre-training.

vs others: More sample-efficient than fine-tuning for one-off tasks, and more flexible than fixed instruction-tuning because examples can be dynamically composed per request

via “few-shot learning and in-context adaptation without fine-tuning”

Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks. It activates 22B of its 235B parameters per forward pass and natively supports up to 262,144...

Unique: Implements in-context learning through the same MoE routing mechanism as main task reasoning, allowing examples to influence expert routing decisions for the main task. This enables the model to learn task-specific expert specializations from context without fine-tuning.

vs others: Faster few-shot adaptation than fine-tuning-based approaches and more flexible than models requiring explicit task-specific training

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

GPT-5.4 mini brings the core capabilities of GPT-5.4 to a faster, more efficient model optimized for high-throughput workloads. It supports text and image inputs with strong performance across reasoning, coding,...

Unique: GPT-5.4 Mini uses a learned ranking function to automatically select and order few-shot examples based on relevance to the current task, rather than requiring manual example curation. The model learns which examples are most informative and orders them to create an optimal learning trajectory, improving few-shot performance without additional training.

vs others: More effective few-shot learning than GPT-4 because automatic example ranking adapts to task-specific patterns; faster than full GPT-5.4 through efficient example selection that reduces context window usage while maintaining learning effectiveness.

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

LFM2-24B-A2B is the largest model in the LFM2 family of hybrid architectures designed for efficient on-device deployment. Built as a 24B parameter Mixture-of-Experts model with only 2B active parameters per...

Unique: LFM2-24B-A2B performs few-shot learning using sparse MoE routing where task-specific experts activate based on example patterns, enabling efficient in-context adaptation without full parameter computation. This allows the model to rapidly adapt to new tasks while maintaining low latency compared to dense models.

vs others: More efficient few-shot adaptation than dense 24B models with lower latency for rapid task switching; comparable few-shot quality to larger models (70B+) while using 1/3 the active parameters, enabling cost-effective multi-task deployments without fine-tuning.

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

MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it...

Unique: Extended context window enables 10-100+ in-context examples compared to typical 2-5 examples in standard models, improving few-shot learning performance without fine-tuning

vs others: More flexible than fine-tuned models (examples can be changed per request) with better few-shot performance than smaller context models, but less effective than task-specific fine-tuning