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.

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.

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.

LLMTabBench: Evaluating LLMs on Binary Tabular Classification From Zero to Few Shots

arXiv:2605. 24417v2 Announce Type: replace Abstract: Supervised classification on tabular data remains a central machine learning task, but its dependence on large labeled datasets limits its applicability in data-scarce settings.

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...

One-shot imitation learning

Exploiting Spatial Structure for Transductive Few-Shot Classification of Whole-Slide Images

The paper introduces SlideTIM, a transductive few‑shot classification method tailored for whole‑slide images (WSIs). SlideTIM extends the LC‑TIM approach by adding a spatial‑latent regularizer and a class‑distribution prior, ensuring that spatially and semantically similar patches receive consistent predictions and that predicted class proportions are calibrated. Experiments on four histology datasets show that SlideTIM outperforms existing TIM variants, boosting macro‑F1 scores by up to 8.1 percentage points over the best baseline and 19.4 percentage points over zero‑shot predictions at one shot.

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.