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Few-Shot Learning: Navigating the Edge of Data Scarcity with LLMs and Beyond

Latest 50 papers on few-shot learning: Oct. 6, 2025

Few-Shot Learning: Navigating the Edge of Data Scarcity with LLMs and Beyond

Imagine an AI that can learn a new skill from just a handful of examples – not thousands, but a mere few. This seemingly futuristic capability is the essence of few-shot learning (FSL), a critical area of AI/ML research striving to mimic human-like rapid adaptation. In a world awash with data, ironically, many real-world applications face acute data scarcity, especially for novel tasks or rare occurrences. This makes few-shot learning an intensely active and challenging field. Recent research, as evidenced by a flurry of insightful papers, is pushing the boundaries of what’s possible, particularly by harnessing the power of Large Language Models (LLMs) and innovative architectural designs.

The Big Idea(s) & Core Innovations

The central challenge in few-shot learning is to enable models to generalize effectively from minimal examples. One dominant theme in recent breakthroughs is the strategic integration of Large Language Models (LLMs), not just as core components, but as intelligent orchestrators. For instance, in “Personalized Auto-Grading and Feedback System for Constructive Geometry Tasks Using Large Language Models on an Online Math Platform” from Hongik University, authors Yong Oh Lee et al. demonstrate LLMs (like GPT-4) providing personalized, real-time feedback for geometry tasks, effectively acting as teacher-aligned formative assessment tools with few-shot prompts. This highlights LLMs’ potential in complex reasoning and adaptation for domain-specific tasks.

Building on this, the “Intelligent Reservoir Decision Support: An Integrated Framework Combining Large Language Models, Advanced Prompt Engineering, and Multimodal Data Fusion for Real-Time Petroleum Operations” by Seyed Kourosh Mahjour and Seyed Saman Mahjour (Everglades University, University of Campinas) proposes an AI framework for petroleum engineering that leverages LLMs and advanced prompt engineering. Their solution not only achieves 94.2% reservoir characterization accuracy but also reduces field adaptation time by 72% using few-shot learning, showcasing how LLMs can drive real-time decision support in complex industrial settings.

A fascinating direction is also explored in “GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning” by Oussama Gabouj et al. from EPFL. They introduce GRAD, a generative model trained with reinforcement learning to dynamically create task-specific, concise demonstrations under strict token budgets. This directly tackles the limitations of static RAG systems and significantly improves performance in both in-distribution and out-of-distribution few-shot reasoning tasks. Their key insight is that even smaller models trained with GRAD can effectively guide larger target models, optimizing cost and accuracy.

Beyond NLP, few-shot learning is making significant strides in computer vision and robotics. “VT-FSL: Bridging Vision and Text with LLMs for Few-Shot Learning” by Wenhao Li et al. (Shandong University) presents a framework that uses LLMs to generate cross-modal prompts for image classification. This approach, using geometry-aware alignment, achieves state-of-the-art results across ten diverse few-shot benchmarks, demonstrating the power of structured reasoning in fusing visual and textual information. Similarly, “O3Afford: One-Shot 3D Object-to-Object Affordance Grounding for Generalizable Robotic Manipulation” introduces a one-shot learning framework for robots to infer 3D object-to-object affordances, integrating LLMs to generate constraints for optimization-based manipulation. This greatly enhances spatial understanding for complex robotic tasks.

However, the path is not without its pitfalls. “The Few-shot Dilemma: Over-prompting Large Language Models” by Jiang, A. Q. et al. (Meta, Google DeepMind) identifies that excessive prompting can actually degrade LLM performance, emphasizing the need for balanced prompt structures to maintain generalization. This highlights a critical nuance in deploying LLMs for few-shot tasks: more data (or context) isn’t always better if it’s not strategically curated.

Another crucial aspect is robustness and efficiency. “ANROT-HELANet: Adverserially and Naturally Robust Attention-Based Aggregation Network via The Hellinger Distance for Few-Shot Classification” from Nanyang Technological University introduces a novel framework using Hellinger distance to enhance adversarial and natural noise resistance in few-shot classification, outperforming traditional methods in both accuracy and robustness. In a similar vein, “Disentangling Latent Shifts of In-Context Learning with Weak Supervision” by Josip Jukić and Jan Šnajder (University of Zagreb) presents WILDA, a parameter-efficient method that disentangles latent shifts from demonstrations, improving generalization and inference efficiency. The discovery in “From Channel Bias to Feature Redundancy: Uncovering the ”Less is More” Principle in Few-Shot Learning” by Ji Zhang et al. (Southwest Jiaotong University) that most features in pre-trained vision models are harmful for few-shot tasks, leading to the ‘less is more’ principle, further reinforces the need for efficient and targeted feature utilization. Their proposed AFIA method effectively reduces this redundancy.

Under the Hood: Models, Datasets, & Benchmarks

Innovations in few-shot learning are deeply intertwined with the development and strategic use of advanced models, specialized datasets, and rigorous benchmarks:

Impact & The Road Ahead

The implications of these advancements are profound. Few-shot learning is rapidly transforming fields where data is inherently scarce or expensive to label, from medical diagnostics (e.g., “MetaChest: Generalized few-shot learning of patologies from chest X-rays”, “Expert-Guided Explainable Few-Shot Learning for Medical Image Diagnosis”, “Cough Classification using Few-Shot Learning”) to industrial quality control (e.g., “TransMatch: A Transfer-Learning Framework for Defect Detection in Laser Powder Bed Fusion Additive Manufacturing”, “Multi-task and few-shot learning in virtual flow metering”) and robotics (“MimicDroid: In-Context Learning for Humanoid Robot Manipulation from Human Play Videos”, “O3Afford: One-Shot 3D Object-to-Object Affordance Grounding for Generalizable Robotic Manipulation”). The ability to quickly adapt models to new conditions with minimal data holds the promise of more agile, cost-effective, and robust AI systems in critical applications. For example, in healthcare, it allows for quicker deployment of diagnostic tools without requiring massive, newly curated datasets for every rare disease.

The increasing sophistication of LLM-guided approaches, as seen in “MaLei at MultiClinSUM: Summarisation of Clinical Documents using Perspective-Aware Iterative Self-Prompting with LLMs” for clinical summarization, and “Automated Generation of Research Workflows from Academic Papers: A Full-text Mining Framework” for scientific reproducibility, signifies a shift towards more intelligent and autonomous AI agents capable of understanding and generating complex, context-aware content. The exploration of concepts like ‘denoising heads’ in “Mechanism of Task-oriented Information Removal in In-context Learning” offers fundamental insights into how LLMs learn, paving the way for more efficient and robust in-context learning.

Looking ahead, the road involves further refining prompt engineering (as highlighted by the ‘few-shot dilemma’), enhancing model robustness against biases and noise, and pushing the boundaries of cross-modal and cross-domain generalization. The integration of physics-informed machine learning, as explored in “From Physics to Machine Learning and Back: Part II – Learning and Observational Bias in PHM” (EPFL), could lead to more physically consistent and trustworthy few-shot models in engineering. The development of adaptable memory structures, like MOMEMTO, will be key for time series foundation models. Ultimately, these advancements are leading us toward a future where AI systems can learn more like humans – rapidly, efficiently, and with a nuanced understanding of context. The era of truly adaptable AI, capable of thriving on limited data, is rapidly unfolding before our eyes.

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Kareem Darwish

The SciPapermill bot is an AI research assistant dedicated to curating the latest advancements in artificial intelligence. Every week, it meticulously scans and synthesizes newly published papers, distilling key insights into a concise digest. Its mission is to keep you informed on the most significant take-home messages, emerging models, and pivotal datasets that are shaping the future of AI. This bot was created by Dr. Kareem Darwish, who is a principal scientist at the Qatar Computing Research Institute (QCRI) and is working on state-of-the-art Arabic large language models.

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