Few-Shot Learning: Unlocking AI’s Potential in Data-Scarce Worlds

Latest 69 papers on few-shot learning: Aug. 25, 2025

In the rapidly evolving landscape of AI and Machine Learning, the quest for models that can learn effectively from minimal data is more critical than ever. This challenge, known as Few-Shot Learning (FSL), is the cornerstone for building adaptable and efficient AI systems, especially in domains where labeled data is scarce or expensive to acquire. Recent breakthroughs, as highlighted by a collection of compelling research papers, are pushing the boundaries of what’s possible, enabling AI to generalize from a handful of examples and tackle complex real-world problems.

The Big Idea(s) & Core Innovations

The overarching theme uniting recent FSL advancements is the ingenious use of pre-trained models, meta-learning, and novel architectural designs to bridge the gap between abundant general knowledge and sparse domain-specific insights. Researchers are moving beyond brute-force data collection, focusing instead on how models learn and transfer knowledge.

One significant innovation comes from the integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) into FSL pipelines. For instance, the University of Virginia team in their paper, “Instruction-based Time Series Editing”, introduces InstructTime, allowing users to modify time series data using natural language instructions. Similarly, “Integrating Time Series into LLMs via Multi-layer Steerable Embedding Fusion for Enhanced Forecasting” by Zhuomin Chen et al. from Sun Yat-Sen University introduces MSEF, enabling LLMs to directly access and retain complex time series patterns across all layers, leading to a remarkable 31.8% reduction in Mean Squared Error (MSE) in forecasting tasks. This is further echoed by East China Normal University and Aalborg University’s “CC-Time: Cross-Model and Cross-Modality Time Series Forecasting”, which fuses PLMs with time-series-specific models for superior accuracy in few-shot scenarios by leveraging textual descriptions.

In computer vision, where data scarcity is a perennial issue, several papers present groundbreaking solutions. Cornell University and Weill Cornell Medicine’s “CoFi: A Fast Coarse-to-Fine Few-Shot Pipeline for Glomerular Basement Membrane Segmentation” leverages lightweight models and automated prompt generation using SAM (Segment Anything Model) for efficient and accurate medical image segmentation with limited data. Addressing fine-grained classification, New York University and Vanderbilt University’s “Glo-VLMs: Leveraging Vision-Language Models for Fine-Grained Diseased Glomerulus Classification” demonstrates that fine-tuning large VLMs can achieve high accuracy with as few as 8 shots per class, especially with pathology-aware backbones.

Meta-learning approaches are also seeing significant innovation. “Overcoming classic challenges for artificial neural networks by providing incentives and practice” by Kazuki Irie and Brenden M. Lake from Harvard and Princeton Universities introduces the Problem of Incentive and Practice (PIP) framework, utilizing metalearning to tackle systematic generalization, catastrophic forgetting, and FSL. This framework also underpins the robustness of LLMs. For a more generalized approach, Usman Anjum et al. from Ottawa University and University of Cincinnati introduce “Domain-Generalization to Improve Learning in Meta-Learning Algorithms” (DGS-MAML), which combines gradient matching with sharpness-aware minimization to boost generalization and convergence in FSL.

Robotics is another area benefiting immensely. Google Research and MIT CSAIL’s “In-Context Iterative Policy Improvement for Dynamic Manipulation” shows how pre-trained LLMs can iteratively improve robotic policies without fine-tuning, outperforming Bayesian Optimization in low-data regimes. Similarly, Tsinghua University’s “H-RDT: Human Manipulation Enhanced Bimanual Robotic Manipulation” uses human manipulation data and a diffusion transformer to achieve substantial improvements in robotic tasks, particularly in few-shot settings.

Under the Hood: Models, Datasets, & Benchmarks

The innovations discussed are often enabled by, or themselves introduce, novel models, curated datasets, and robust benchmarks. These resources are critical for validating new techniques and fostering further research.

• Multi-layer Steerable Embedding Fusion (MSEF) (code): A framework that allows LLMs to directly access time series patterns at all depths, improving forecasting performance on datasets like Electricity Load Diagrams, Jena Weather, and PEMS.
• CoFi Pipeline (code): A fast few-shot segmentation pipeline for medical images, particularly effective for Glomerular Basement Membrane (GBM) delineation using electron microscopy images.
• GLiClass (code): A lightweight, label-conditioned encoder transformer for sequence classification that supports zero-shot and few-shot learning, offering superior accuracy-latency trade-offs.
• MIST (Multiple Stochastic Prompt Tuning): A framework for adapting CLIP to extreme domain and semantic shifts using class-specific prompts modeled as Gaussian distributions.
• DGS-MAML (code): A meta-learning algorithm combining gradient matching and sharpness-aware minimization for improved generalization in few-shot scenarios.
• MultiADS (code): A zero-shot learning approach for multi-type anomaly detection and segmentation, leveraging defect-specific knowledge from pre-trained VLMs.
• Causal CLIP Adapter (CCA) (code): Enhances few-shot learning by using causal disentanglement with ICA and cross-modal alignment, validated across 11 benchmark datasets.
• PointKAN (code): A Kolmogorov-Arnold Network (KAN) based architecture for point cloud analysis, demonstrating parameter efficiency and strong performance on ModelNet40 and ScanObjectNN.
• MicroMix (code): A mixed-precision quantization algorithm based on Microscaling (MX) data formats that boosts LLM efficiency and accuracy in zero-shot tasks.
• InstructTime (code): A novel time series editor that uses natural language instructions for multi-resolution editing, generalizing to unseen instructions.
• MOFS: A multi-operator few-shot operator learning framework that integrates frequency-aware self-supervision, semantic text conditioning, and memory-augmented multimodal prompting for generalization across PDE families.
• M3FD Dataset & M3F Framework (code): A multi-modal few-shot dataset with over 10K samples and a framework built on LMMMs for FSL in scientific domains, featuring a 4-Stage Training Strategy.
• GraphProp: The first Graph Foundation Model (GFM) that achieves both structural and node feature generalization across domains, particularly effective when graphs lack node attributes.
• LoopDB Dataset (code): A new benchmarking dataset introduced alongside LoopNet for evaluating loop closure detection in large-scale SLAM systems under varying conditions.
• CodeMixEval: A comprehensive framework for evaluating LLMs on code-mixed data across 18 languages.
• P-CoT (code): A pedagogically-motivated Chain-of-Thought prompting method that enhances LLM performance on phonological reasoning tasks.
• CPLSR (code): A Cross-Domain Few-Shot Learning approach combining Coalescent Projections and Latent Space Reservation for handling extreme domain shifts.
• GLAD: A parameter-efficient fine-tuning framework that leverages LoRA with gradient-based regularization for robust generalization of VLMs in few-shot settings.
• FMC Dataset (code): A high-quality dataset of 3,922 natural language–Lean pairs sourced from Olympiad problems, serving as a benchmark for automated theorem provers.

Impact & The Road Ahead

These advancements in few-shot learning are poised to democratize AI development, making advanced capabilities accessible even in data-scarce sectors like healthcare, robotics, and environmental monitoring. The ability of models to learn from minimal examples means faster deployment, reduced annotation costs, and more robust systems that can adapt quickly to changing environments or new tasks.

The trend of leveraging pre-trained large models (LLMs, VLMs) and meta-learning for rapid adaptation is particularly exciting. This paradigm shift suggests that future AI systems will be less about training monolithic models from scratch and more about intelligently transferring and fine-tuning existing knowledge. Key challenges remain, such as ensuring generalization robustness across extreme domain shifts (as explored in “Multiple Stochastic Prompt Tuning for Few-shot Adaptation under Extreme Domain Shift” and “Cross-Domain Few-Shot Learning with Coalescent Projections and Latent Space Reservation”), improving model interpretability (“Closed-Form Feedback-Free Learning with Forward Projection”), and safeguarding against vulnerabilities like tool poisoning attacks (“MCPTox: A Benchmark for Tool Poisoning Attack on Real-World MCP Servers”).

The horizon holds promising developments: more versatile multi-modal foundational models like Oregon Health & Science University’s M3F (“A Foundational Multi-Modal Model for Few-Shot Learning”) will unlock scientific discovery, while bio-inspired strategies like “Color as the Impetus: Transforming Few-Shot Learner” from Harbin Institute of Technology will lead to more intuitive and effective learning. The path forward involves refining adaptation techniques, building even richer general knowledge models, and ensuring that these powerful, efficient AI systems are both robust and trustworthy. The era of truly adaptable AI, capable of learning and innovating with human-like efficiency, is rapidly approaching.