Tag
A systematic study of LLM-based machine translation evaluator design — instruction structure, few-shot demonstrations, backbone choice, and multi-step workflows — introducing Orbit, a family of LLM MT evaluators that achieve expert-level error span detection and ranking at roughly 20× lower cost than human annotation.
提出 silver-evidence demonstrations 方法，用生成的、目标条件化的证据摘要替换 ICL 示例中的原始检索段落，以减少噪声检索带来的干扰。该方法在 EI 2.0、SciFact、合成策略检索和 HICRIC/IMR 数据集上优于 gold-only ICL 和 InstructRAG 式 ICL，并在所有标注数据集上提升了证据 F1。
该论文提出 EGiME（熵引导模型评估），一种基于特征空间聚类结构的半监督准则，无需分类器训练即可在少样本场景下评估预训练模型，在五个基准上与真实准确率相关性达 0.966，并比有监督基线提供更稳定的模型排名。
This paper shows that naive few-shot LoRA personalization on-device degrades out-of-scope (OOS) rejection AUROC across five intent-classification benchmarks while preserving in-scope accuracy, and proposes a training-free fix using Mahalanobis-distance-based rejection scoring that recovers most of the lost quality on 3 of 5 datasets.
提出CofFeQA，一种基于反事实样本的少样本知识蒸馏方法，用于开放域问答任务；在TriviaQA等基准上，相比仅用事实样本的蒸馏基线在T5设置下以32个少样本对提升约8.77 EM和9.98 F1分。
论文提出用零训练的 System-One LLM（Jev）直接对学习者状态输出答对概率，在无任何目标平台数据时 AUC 达 .706，超过 28 个深度 KT 模型在 8 名学习者上训练的结果以及 System-Two 推理式 KT，且成本仅约 1/100；加入 64-shot 示例和近邻统计后（JevKT）提升至 .722，在约 64 名学习者以内保持领先。
The paper proposes three approaches for few-shot author-style adaptation of LLMs—contrastive activation steering, a steering-vector prediction network, and a hypernetwork that predicts LoRA adapters—and finds the hypernetwork offers the best trade-off between style imitation and output quality for both seen and unseen authors.
A new paper proposes Dual-Stream OSSE-LSTM, an episodic metric-learning framework combining an Omni-Scale CNN with a Bidirectional LSTM for label-efficient time series classification, plus Counterfactual Integrated Gradients (C-IG) for explainable prototype-based decisions, achieving strong and stable accuracy on 19 UCR datasets with only 2-8 labeled examples per class.
This paper explores adapting prior-data fitted networks (PFNs) for tabular anomaly detection, proposing a fine-tuning-free approach (ZEN) using frozen TabPFN features with nearest-neighbor scoring, and a fine-tuned method (FOCUS) that surpasses all baselines on the ADBench benchmark.
This paper presents the first empirical study evaluating Jev, a general-purpose decision model, for network traffic application classification using only early-flow packet features on the CESNET-QUICEXT-25 dataset. Labeled examples boost Jev's accuracy from 9.80% to 34.50%, but trained tree ensembles and GPT-5.6 Sol still outperform it, suggesting labeled in-context examples alone are insufficient to match dedicated classifiers.
This paper studies how much normal evidence (images or categories) is needed to certify a false-alarm-rate-controlled threshold for few-shot industrial anomaly detection. Using a frozen DINOv2 PCA residual ranker on MVTec and VisA, the authors show target-only calibration is shift-fragile, derive lower bounds on the number of iid categories needed for valid distribution-free 95% upper confidence bounds (14–59 draws), and propose the CRESS protocol that largely fails closed with only a few certification categories.
BARRAC adapts an English aspect-based sentiment analysis framework to Arabic dialect classification tasks by substituting consumer-review attribute pools with Arabic linguistic devices for sarcasm, dialect, and sentiment, achieving 63.93% mean macro-F1 and outperforming GPT-4o on four of five benchmarks.
ENCORE 提出了一种少样本智能体框架，将机器人操作演示蒸馏为可供编码智能体阅读的证据包，从而自动编写策略程序，在 LIBERO-PRO 上以 96.3% 超过此前最优智能体系统，并在真实双臂机器人上完成魔方交接与杯子翻转任务。
A study or model with only 4 million parameters, fine-tuned on 10,000 examples, achieves performance surpassing opus and kimi in certain benchmarks.
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.