zero-shot-learning

This academic paper investigates using LLMs for zero-shot prediction of psychological well-being scores from spontaneous speech, evaluating 12 models and achieving high correlation with clinical metrics.

This paper introduces the Neural Rule Inducer (NRI), a foundation model for zero-shot logical rule induction that uses domain-agnostic statistical properties to generalize across tasks without retraining.

DiZiNER is a framework that uses disagreement between multiple LLMs to refine task instructions for zero-shot named entity recognition, achieving state-of-the-art results on 14 out of 18 benchmarks and significantly reducing the performance gap between zero-shot and supervised systems.

Researchers introduce Zero-shot World Models (ZWM), an approach that achieves visual competence comparable to state-of-the-art models while trained on minimal data (single child's visual experience) without task-specific training. This work demonstrates a path toward more data-efficient AI systems that match human developmental learning efficiency.