few-shot learning
few-shot-learning-6c94fc52·3 events·first seen 28d agoAliases: few-shot learning
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Language models are few-shot learners
OpenAI published the GPT-3 paper introducing a 175-billion-parameter autoregressive language model demonstrating strong few-shot learning capabilities across a wide range of NLP tasks. The work showed that scaling language models dramatically improves task-agnostic, few-shot performance, often matching or exceeding fine-tuned models without any gradient updates. This paper became a foundational milestone in the development of large language models and the modern AI landscape.
Learning Concepts with Energy Functions
OpenAI presents an energy-based model capable of learning abstract spatial concepts—such as 'near,' 'above,' and 'between'—from only five demonstrations using sets of 2D points. The model generalizes across domains, transferring concepts learned in a 2D particle environment to control tasks in a 3D physics-based robot simulation. The work demonstrates few-shot concept acquisition and cross-domain transfer via energy function representations.
Foundation Model for Wearable Health Data Pretrained on 1 Trillion Minutes from 5 Million Participants
Researchers propose a large-scale foundation model for wearable health data, pretrained on over one trillion minutes of unlabeled sensor signals from five million participants. The model demonstrates systematic performance improvements across 35 health prediction tasks spanning cardiovascular, metabolic, sleep, and mental health domains, with joint scaling of model capacity and data volume. A 'classroom' of LLM agents autonomously searches downstream predictive head configurations, and the resulting embeddings are integrated into a Personal Health Agent validated by 1,860 clinician ratings. The work establishes label-efficient few-shot learning and generative capabilities for daily health metric estimation.