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

SetFit: Efficient Few-Shot Learning Without Prompts

Language models are few-shot learners

Neuron-Aware Active Few-Shot Learning for LLMs

arXiv:2607. 02423v1 Announce Type: cross Abstract: Active Few-Shot Learning (AFSL) adapts LLMs to specialized domains by identifying the most valuable unlabeled samples for annotation and use as few-shot demonstrations, effectively reducing human annotation costs while promoting high performance.

PRiSM: Prototype Regularization for Few-Shot VLMs

Training-free few-shot adaptation methods have gained significant attention recently in the context of Vision-language Models (VLMs). Yet, current benchmarks rely on strong assumptions about the statistics of the adaptation data, e.

Proposal Refinement for Few-Shot Object Detection

arXiv:2606. 09245v1 Announce Type: cross Abstract: Few-shot object detection has gained widely attention in recent years.

Few-Medoids: An Embarrassingly Simple Coreset Selection Method for Few-Shot Knowledge Distillation

arXiv:2607. 05891v1 Announce Type: cross Abstract: Coreset selection aims to identify a small and highly representative subset of a massive dataset for efficient model training.