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Few-Shot Learning Techniques

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एका दृष्टिक्षेपात

Few-shot learning enables models to learn from very few examples (2-10 per class). Instead of needing 1000 labeled samples, you provide a handful and the model generalizes. With LLMs, few-shot means embedding examples in the prompt context. With classical ML, it's transfer learning + meta-learning frameworks. Practitioners earn 20-30% premium because few-shot unblocks ML in data-scarce domains (healthcare, regulatory, niche industries). Mastery takes 3-4 weeks for LLM prompting, 8-12 weeks for advanced meta-learning.

Few-Shot Learning Techniques म्हणजे काय

Few-shot learning is the ability to train or adapt a model from very limited labeled data (typically 2-20 examples). Two main approaches: (1) LLM prompting, embed examples in the prompt context and let the model infer from them without weight updates; (2) Meta-learning, train a model on many tasks such that it learns to adapt quickly when given a handful of new examples. Few-shot with LLMs is often called in-context learning (ICL). You provide the model with a few demonstrations (input-output pairs) and it generalizes to new inputs, all within a single inference call. No fine-tuning required.

🔧 साधने आणि परिसंस्था
LLM APIs (Claude, GPT, Llama)Prompt engineering frameworksFew-shot prompting (CoT, ICL)Transfer learning librariesMeta-learning frameworks (MAML, Prototypical Networks)PyTorchHugging Face TransformersFew-shot benchmarks (Omniglot, miniImageNet)

💰 प्रदेशानुसार पगार

प्रदेशज्युनियरमध्यमसीनियर
USA$85k$145k$220k
UK£50k£88k£135k
EU€55k€95k€145k
CANADAC$90kC$155kC$230k

❓ FAQ

What's the difference between few-shot and zero-shot?
Zero-shot: no examples provided, model must understand task from description alone ('Classify this email as spam or not'). Few-shot: provide 2-10 examples in context. Few-shot is usually 10-20% more accurate. Few-shot costs more tokens but often worth it.
How many examples do you need in few-shot?
Depends on task complexity. Simple (binary classification): 2-4 examples per class. Complex (reasoning task): 8-16 examples. Rule of thumb: start with 4, measure accuracy, add more if it plateaus. Diminishing returns after 10 examples.
Does few-shot work with open-source LLMs?
Yes, but less reliably than GPT-4 or Claude. Smaller models (7B) struggle with 10-shot. Larger models (70B) handle it better. Proprietary APIs are more robust. Test empirically on your task.
What's in-context learning and why is it few-shot?
In-context learning (ICL): model learns from examples in the prompt without updating weights. This is few-shot. Contrast: fine-tuning (update weights with gradient descent). ICL is zero-cost deployment, fine-tuning has setup cost but can be more accurate on narrow tasks.
How do you order few-shot examples in a prompt?
Order matters. Research shows: (1) similar examples first (closest match to test input), (2) diverse examples (cover different categories), (3) arrange by complexity. Experiment. Sometimes random order works; sometimes deliberate matters.

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