few-shot-learning

This paper presents SRCF, an attack that steers Large Reasoning Models (LRMs) via counter-aligned few-shot conversations to cause unsafe or refusal behaviors, and proposes ARCF, a post-training defense that enhances safety and helpfulness without degrading utility.

ClusterFewshot is a novel method for improving few-shot demonstration selection in LLM workflows by integrating semantic clustering and utility scoring, which reduces optimization costs and enhances accuracy in DSPy-based pipelines.

The paper introduces a two-model architecture called Summarize-Judge-Refine (SJR) for multimodal content moderation, which decouples content understanding and policy learning via natural language summaries, enabling significant performance gains and few-shot policy adaptation.

A user shared a production use case where the Jev tool efficiently retagged 2,300 AI research papers with high accuracy and low cost, improving upon previous classification methods.

The paper introduces Embed-TTT, a two-step test-time training protocol that improves rule induction in ARC-like tasks by first finetuning task embeddings and then the backbone, leading to better alignment with underlying rules and enhanced performance on benchmarks like ARC-AGI-1 and ConceptARC.

This paper challenges the distortion hypothesis for few-shot degradation in language models by introducing a random-text control, showing that representation shift is largely due to prompt length, and models with higher content delta benefit more from few-shot prompting.

ALPINE introduces an ultra-lightweight spatial-relational architecture for few-shot image classification that achieves accuracy gains with fewer parameters, faster convergence, and better robustness compared to baselines like Prototypical Networks and MAML.

The paper proposes the Convergent Emergence Hypothesis, stating that few-shot in-context learning emerges with a common cross-modality difficulty profile, and provides empirical support through experiments on six modalities, showing correlated effects in five.

The paper introduces Iterative Sequential Transfer (IST) to address knowledge transfer challenges in few-shot multiobjective multitask optimization under tight evaluation budgets, using likelihood-informed task prioritization.

IBM researchers introduce STAIR, a generative retriever that uses table of contents to preserve document structure, achieving 82.6% Recall@1 and reducing hallucination in RAG systems.

PiPMRE is a novel pipeline framework for medical relation extraction that uses a relation generator and filter to enhance performance, surpassing previous state-of-the-art methods on public datasets.

The Gen1-5 robot foundation model exhibits unprecedented capabilities in real-time learning, few-shot learning, and physical generalization. It can swiftly acquire new tasks from brief demonstrations and spontaneously create and utilize tools.

This paper presents Aslema, a system for the NADI 2026 shared task on spoken language understanding, using fine-tuned audio LLMs and synthetic data augmentation to improve intent recognition and slot filling for Tunisian Derja.

The author describes a breakthrough where a robot using GEN-1.5 model imitated tasks after a single demonstration without fine-tuning, marking a significant advance in few-shot learning for physical prompting.

This paper proposes a geometric filtering framework that selects high-quality LLM-generated samples by evaluating their Euclidean distance to real class examples in an embedding space, improving few-shot text classification performance by +2.61 percentage points over SMOTE.

This paper introduces PAC-Bayes-regularized Meta-LoRA for cross-domain LLM personalization, enabling zero- and few-shot adaptation to user preferences while preventing overfitting under sparse evidence. Experiments on benchmarks like HiCUPID show significant improvements in cross-domain win rates and cold-start scenarios.

This paper introduces DyRIS, an LLM-agent framework using dynamic few-shot retrieval and rule-guided inference to predict space groups of double perovskites, achieving competitive accuracy and significantly improving performance on minority space-group classes.

FLARE is a new framework that combines few-shot learning with reflective mechanisms to optimize instructions for LLMs, outperforming GEPA across multiple benchmarks including HotPotQA, tool calling, and GoEmotions.

This paper surveys and benchmarks NLP-based automatic deception detection in legal contexts, comparing fine-tuned transformers and seven LLMs with various prompting strategies across seven datasets. Results show domain sensitivity, with fine-tuned models excelling in general domains and few-shot LLMs competitive in low-resource legal settings.

DIRECT is a framework for sequence labeling using large language models that improves domain alignment through Direct Preference Optimization (DPO) after supervised fine-tuning and increases inference efficiency via controlled decoding with template-filling and KV cache reuse.