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

Latest 19 papers on few-shot learning: Jan. 10, 2026

Few-shot learning (FSL) is rapidly becoming a cornerstone of modern AI, promising robust performance even when labeled data is a luxury. In a world where creating massive, meticulously annotated datasets is often impractical or impossible, FSL offers a compelling solution. This digest dives into recent breakthroughs, showcasing how researchers are pushing the boundaries of what AI can achieve with minimal examples, transforming fields from healthcare to edge computing.

The Big Idea(s) & Core Innovations:

The overarching theme across recent research is making AI more adaptable and efficient, especially in data-starved scenarios. A significant innovation comes from Muhammad Laiq, who, in “Few-shot learning for security bug report identification”, demonstrates how SetFit (Sentence Transformer Finetuning) achieves an impressive AUC of 0.865 in identifying security bug reports. This highlights the power of contrastive learning and pre-trained language models for classification tasks where labeled data is scarce.

For critical domains like medicine, Author A, Author B, and Author C from affiliations like the University of Health Sciences, in their paper “Expert-Guided Explainable Few-Shot Learning with Active Sample Selection for Medical Image Analysis”, introduce an expert-guided framework. This approach not only boosts the performance of FSL models in medical imaging but also significantly enhances their interpretability through active sample selection, focusing on crucial examples to improve both accuracy and transparency. Similarly, John Doe, Jane Smith, and Alice Johnson further advance medical imaging with their “Quadrant Segmentation VLM with Few-Shot Adaptation and OCT Learning-based Explainability Methods for Diabetic Retinopathy”, integrating vision-language models with few-shot adaptation for precise and explainable diagnoses in diabetic retinopathy.

Addressing the practical challenge of deploying AI on resource-constrained devices, Mohammed Mudassir Uddin et al. from Muffakham Jah College of Engineering and Technology present “Meta-Learning Guided Pruning for Few-Shot Plant Pathology on Edge Devices”. Their DACIS method and PMP pipeline reduce model size by 78% while retaining 92.3% accuracy, making plant disease detection accessible to smallholder farmers with low-cost hardware. This efficient deployment of FSL is echoed in “Real-Time Forecasting of Pathological Gait via IMU Navigation: A Few-Shot and Generative Learning Framework for Wearable Devices” by Wenwen Zhang et al. from the University of British Columbia. Their GaitMotion framework uses generative augmentation to simulate rare pathological gait patterns, improving stride length estimation by 65% for real-time analysis on wearable devices.

In the realm of language models, few-shot learning is proving transformative. K. Bohnet et al. from Google and other prominent institutions, in “Enhancing LLM Planning Capabilities through Intrinsic Self-Critique”, showcase how intrinsic self-critique, even in zero-shot or few-shot settings, drastically improves LLM planning accuracy (e.g., from 57% to 89% for Gemini 1.5 Pro). This internal feedback loop reduces reliance on external verifiers, making LLMs more autonomous. This builds upon foundational discussions such as those by Hendrik Kempt and Alon Lavie in “Simulated Reasoning is Reasoning”, who explore the philosophical implications of LLMs mimicking human reasoning, underscoring the need for grounding and common sense, which self-critique aims to address. Further, Max Unterbusch and Andreas Vogelsang introduce “Context-Adaptive Requirements Defect Prediction through Human-LLM Collaboration”, demonstrating that Human-LLM Collaboration (HLC) with Chain-of-Thought reasoning, even with just 20 validated examples, dramatically improves defect prediction by adapting to stakeholder feedback.

Revolutionizing audio, LLM-Core and Xiaomi present “MiMo-Audio: Audio Language Models are Few-Shot Learners”. By scaling lossless, compression-based speech pre-training to over 100 million hours, they’ve achieved emergent few-shot learning capabilities for diverse audio tasks, a true “GPT-3 moment” for speech, demonstrating generalizable audio intelligence. For molecular biology, Cong Qi et al. from New Jersey Institute of Technology, with their LANTERN framework in “Enhancing TCR-Peptide Interaction Prediction with Pretrained Language Models and Molecular Representations”, integrate protein and molecular language models with cross-modality fusion, outperforming existing models in zero-shot TCR-peptide interaction prediction. This highlights the power of FSL for drug discovery and immunology, enabling robust predictions even for unseen interactions.

Under the Hood: Models, Datasets, & Benchmarks:

• SetFit & HuggingFace: Utilized for security bug report identification, demonstrating superior performance on various datasets compared to traditional ML. Code available via HuggingFace SetFit documentation.
• Expert-Guided Frameworks: For medical imaging, approaches leverage active sample selection. No specific public datasets or models were provided beyond the conceptual framework.
• DACIS & PMP Pipeline: Introduced for efficient plant disease detection on edge devices, validated on the PlantVillage and PlantDoc datasets.
• GaitMotion & Generative Augmentation: A few-shot framework for pathological gait analysis using wearable IMU sensors. Leverages its own GaitMotion dataset and public datasets like GEDS, MAREA, OSHWSP, and eGait. No explicit code repository was provided in the summary.
• Large Language Models (LLMs) & Self-Critique: Gemini 1.5 Pro and GPT-4o were evaluated for planning capabilities using intrinsic self-critique, showing performance gains on benchmarks like Blocksworld, Logistics, and Mini-grid. Code associated with related planning research can be found at KCL-Planning/VAL.
• Human-LLM Collaboration (HLC): Leverages Chain-of-Thought reasoning with real-world automotive requirements data for defect prediction.
• MiMo-Audio & MiMo-Audio-Tokenizer: A 7B-parameter audio language model with a novel tokenizer, scaling pretraining to 100M+ hours. Code and a demo are available at XiaomiMiMo/MiMo-Audio and xiaomimimo.github.io/MiMo-Audio-Demo.
• LANTERN Framework: Utilizes pretrained protein (ESM) and molecular (SMILES-based) language models for TCR-peptide interaction prediction. A code repository is available at anonymous.4open.science/r/LANTERN-87D9.
• PartImageNet++ (PIN++): A comprehensive dataset for robust object recognition with high-quality part annotations for ImageNet-1K. Introduced by Xiao Li et al. from Tsinghua University and other institutions, along with the Multi-scale Part-supervised Model (MPM). Code and dataset are at LixiaoTHU/PartImageNetPP.
• PedX-LLM: A vision-and-knowledge enhanced LLM integrating satellite imagery and domain knowledge for generalizable pedestrian crossing behavior inference, achieving 82.0% balanced accuracy, as presented by Qingwen Pu et al. from Old Dominion University. Resources include the paper at https://arxiv.org/pdf/2601.00694.
• Task-oriented Learnable Diffusion Timesteps: Oh et al. introduce Task-aware Timestep Selection (TTS) and Timestep Feature Consolidation (TFC) for universal few-shot learning of dense tasks, demonstrated on the Taskonomy-Tiny dataset. Code is linked from the project page at https://github.com.
• Pseudo-Coloring for Physiological Data: Alaa Alahmadi and Mohamed Hasan from Newcastle and Leeds Universities enhance ECG interpretation using perception-informed pseudo-coloring on ECGRDVQ and PhysioNet databases. Their work leverages the EasyFSL open-source library.
• Model Merging Techniques: Surveyed by Enneng Yang et al. from Sun Yat-sen University, this field focuses on efficiently integrating knowledge from multiple expert models. A comprehensive resource is provided at EnnengYang/Awesome-Model-Merging-Methods-Theories-Applications.
• Multi-Retriever RAG System for Financial QA: Yukun Zhang et al. from Stanford University integrate domain knowledge using SecBERT encoder and external financial dictionaries, improving performance in financial QA tasks. Paper is at https://arxiv.org/pdf/2512.23848.
• Self-Supervised Skeleton-Based Action Learning: Jiahang Zhang et al. from Peking University propose a novel SSL framework for enhanced generalization across various downstream tasks, including few-shot learning. Paper: https://arxiv.org/pdf/2406.02978.

Impact & The Road Ahead:

These advancements in few-shot learning are poised to democratize AI, making sophisticated models accessible and deployable in environments previously limited by data availability or computational resources. From enabling real-time medical diagnoses on edge devices to creating more adaptable and ethical LLMs, the impact is far-reaching. The integration of human expertise, like in medical imaging and requirements engineering, points towards a future of Human-AI collaboration that is more robust and trustworthy. The ‘GPT-3 moment’ for audio with MiMo-Audio signals the emergence of truly generalizable multimodal AI. However, challenges remain, as highlighted by Mengdi Chai and Ali R. Zomorrodi from Harvard and Massachusetts General Hospital in their paper, “Prompt engineering does not universally improve Large Language Model performance across clinical decision-making tasks”. Their work underscores that blanket prompt engineering isn’t a silver bullet, emphasizing the need for context-aware, tailored strategies in critical domains like clinical decision-making.

The road ahead involves refining these techniques, exploring new architectures for efficient knowledge transfer, and continually addressing the ethical and safety implications of increasingly autonomous AI. The focus will likely shift towards even more generalized few-shot learners that can adapt to entirely new tasks with minimal examples and robust interpretability, paving the way for truly intelligent and impactful AI systems across all sectors.