SetFit: Efficient Few-Shot Learning Without Prompts

Few-shot learning in practice: GPT-Neo and the 🤗 Accelerated Inference API

Language models are few-shot learners

Are We Really Doing Few-Shot Learning? A Critical Examination of Pre-Training Assumptions

The paper critically evaluates common few‑shot learning protocols that rely on pre‑training a model on a large auxiliary set with classes disjoint from the target but drawn from the same visual domain. By comparing no pre‑training, class‑disjoint in‑domain pre‑training, supervised out‑of‑domain pre‑training, and label‑free out‑of‑domain pre‑training across eight datasets and three architectures, the authors find that in‑domain pre‑training yields a 33.41‑point average improvement, while out‑of‑domain pre‑training offers a 23.75‑point gain, revealing a 9.66‑point optimistic bias due to domain overlap. They also demonstrate that a label‑free augmentation strategy can match supervised out‑of‑domain performance and propose a descriptor‑based source‑selection method that closely approximates oracle selection, underscoring the need to move beyond in‑domain pre‑training as the default evaluation protocol.

Training-free LLM Verification via Recycling Few-shot Examples

arXiv:2506.17251v3 Announce Type: replace-cross Abstract: Although large language models (LLMs) have achieved remarkable performance, the inherent stochasticity of their reasoning processes and varyi...

Re-Evaluating Continual Learning with Few-Shot Adaptation

arXiv:2606. 03843v1 Announce Type: cross Abstract: Continual learning methods aim to maximize the stability and plasticity of machine learning models that are trained on a sequence of tasks.

One-shot imitation learning