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#few-shot

3 curated events
papersSEP 10 04:00 UTC

Balanced k-shot sampling causes exact degeneracy in discriminant analysis on LLM embeddings

A new arXiv paper proves that balanced k-shot sampling, which draws exactly k labeled examples per class, induces an exact and provable degeneracy in a family of small-sample discriminant estimators. The result concerns kernelized linear discriminant methods applied to LLM embeddings, where the within-class scatter operator breaks down under equal per-class sample counts. The finding carries practical consequences for few-shot classification pipelines that rely on embeddings from large language models.

llm-embeddings/few-shot/statistics/classification
papersSEP 12 04:00 UTC

Paper Questions Whether Few-Shot Learning Protocols Reflect True Few-Shot Conditions

A new arXiv paper argues that standard few-shot learning benchmarks may not measure what they claim. In typical setups, models are first trained on a large auxiliary dataset whose categories differ from the evaluation episodes but come from the same visual domain, so the target task is not genuinely novel. The authors call for closer scrutiny of these pre-training assumptions when interpreting few-shot results.

few-shot/pre-training/benchmarks/research
papersSEP 12 04:00 UTC

Soft Prompting Approach Adapts Vision-Language Models with Few Shots

A new arXiv paper tackles few-shot object detection with vision-language models in domains that differ from their training data, such as aerial, industrial, and medical imagery, where only ten labeled images are available. Rather than optimizing discrete text prompts, the authors propose a soft prompting method for adaptation. The work is a cross-listing on arXiv cs.AI.

vision-language/few-shot/prompting/object-detection