Editing Few Shot Zero Shot (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
'''Zero-shot learning''' with CLIP: Train a model to align image and text representations. At inference, compute the image embedding and compare it against text embeddings of all possible class descriptions ("a photo of a cat", "a photo of a dog"). The class with the highest similarity is the prediction — without ever training on these specific classes.

'''Why does zero-shot work?''' CLIP was trained on 400M image-text pairs. Through this training it has learned that images of dogs and text about dogs inhabit similar regions of embedding space. At zero-shot time, new class descriptions ("a photo of a Tibetan Mastiff") can be correctly associated with unseen images because the semantic alignment was learned during pre-training.

'''In-context few-shot learning''': GPT-4 can learn to perform a new task from 3-5 examples in the prompt — no gradient updates. The model recognizes the pattern in the examples and continues it for new inputs. This is surprisingly powerful for classification, translation, format conversion, and reasoning tasks.

'''The few-shot learning / meta-learning connection''': Few-shot learning and meta-learning address the same problem from different angles. Meta-learning trains a model explicitly to learn from few examples (gradient-based: MAML; metric-based: Prototypical Networks). LLM in-context learning achieves similar results without explicit meta-training — an emergent capability.

'''Retrieval-augmented zero-shot''': When semantic class descriptions aren't available, retrieve relevant documents at inference time and use them to ground predictions — extending the model's effective knowledge without fine-tuning.
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