few-shot

SkalskiP highlights Qwen3.8-Max, a vision-language model for object detection that can be prompted with positive and negative boxes to generate detections, achieving 60-80% mAP with single or multiple prompts and performing well on diverse image types.

Proposes DuPLeR, a dual-path LLM reasoning framework for multimodal few-shot knowledge graph completion, combining LLM-derived type priors with factual structures to improve inductive KGC under data scarcity.

This paper introduces cross-domain off-policy evaluation and learning (OPE/L) for contextual bandits, allowing the use of logged data from multiple source domains to improve policy evaluation and learning in target domains with challenging conditions like few-shot data, deterministic logging policies, and new actions.

Introduces DriveDNA, a large-scale multimodal naturalistic driving dataset with 4,121 drives from 465 drivers across 115 vehicle models, and a benchmark for driving style identification through tasks like few-shot driver re-identification and personalized behavior prediction.

This paper studies how LLM adaptation strategies (definitions, examples, fine-tuning) transfer under source shift in climate disclosure classification, finding that simpler strategies like definitions transfer more consistently than complex ones.

This paper presents a comprehensive evaluation of five large language models for citation function classification, achieving new state-of-the-art results on the ACL-ARC dataset with a fine-tuned Falcon 7B model. It also introduces the AC3 dataset, which includes a seven-category annotation scheme distinguishing neutral acknowledgments from evaluative stances.

Fable 5 model only used 4 prompts and $173 worth of tokens to create a game called 'Super Smart Racing', demonstrating its extremely strong generative capabilities.

This paper challenges the assumption that reranking always improves few-shot selection for LLMs, proposing a training-free gated reranking approach that uses model uncertainty to decide when to rerank, reducing computational costs by 15-80% while slightly improving performance.

This paper compares fine-tuned BERT (gbert-large) with few-shot LLM prompting (Llama 4 Maverick) for detecting threat and solution framing in German climate news sentences. BERT achieves higher F1 scores (0.83 vs 0.78), and an ablation study shows that providing preceding sentence context improves performance.

Introduces AnySimLite, a lightweight similarity encoder for on-device speech-adjacent classification tasks, achieving state-of-the-art or competitive performance while using less than 1/250th the model size of the qLLaMA-LoRA-7B baseline.

This study investigates whether instruction-tuned LLMs (Llama-3.1-8B, Qwen2.5-7B, Mistral-7B, Phi-3-mini) can reliably classify Correct Information Units in aphasic discourse transcripts. Few-shot prompting yields competitive F1 scores (0.776–0.817) for three models, but performance varies by severity and human agreement remains insufficient for fully autonomous use.

This paper investigates few-shot biomedical relation extraction using prompt-based learning with LLMs, comparing pairwise classification and joint generation approaches. The best model achieves micro-F1 of 0.44, outperforming previous few-shot results but remaining below supervised baselines, while macro-F1 surpasses the supervised baseline on rare relation types.

PrintGuard 2.0 is a major rewrite of a few-shot FDM fault detector using a ShuffleNetV2 backbone and prototypical network, now with a single Python engine that runs unmodified on both CPython and Pyodide in the browser via a platform abstraction layer, enabling per-printer sensitivity tuning and fair inference scheduling.

This paper proposes LLM-GNN Co-Teaching, a bidirectional framework for few-shot graph learning on text-attributed graphs. The LLM and GNN exchange confident pseudo-labels and use round-based preference optimization (RPL-PO) to mutually improve, outperforming prior methods on benchmarks.

Proposes Demo2Reward, a test-time prompt optimization technique for VLM reward models using a few expert demonstrations, significantly reducing false positives and improving policy learning in robotics without additional model training.

This paper presents a hybrid framework that combines structured clinical data with LLM-generated narratives for coronary artery disease prediction, achieving high fidelity in variable extraction and comparing ML models with LLM-based zero-shot and few-shot classification.

GraphARC is a new benchmark for abstract reasoning on graph-structured data, extending the ARC paradigm to graphs. Evaluations of state-of-the-art language models reveal a comprehension-execution gap and performance degradation on larger instances, highlighting scaling challenges.

This paper introduces ACIL, an automatic Chain-of-Thought framework to enhance In-Context Learning by generating and pruning reasoning chains, improving LLM performance on complex tasks.

This paper explores using few-shot prompted LLMs for actionable triage categorization of online patient inquiries into self-care, schedule-visit, urgent-clinician-review, or emergency-referral. The best model (Claude Haiku 4.5 with 12-shot prompting) achieves macro-F1 of 0.475, surpassing supervised baselines, but the authors conclude that LLMs can support triage prioritization and selective human review, not autonomous deployment.

FFAvatar proposes a feed-forward framework for reconstructing high-quality, animatable 3D Gaussian head avatars from few unposed images in seconds, achieving a 5.5 PSNR improvement over state-of-the-art on the NeRSemble benchmark.