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7 results for “zero-shot”
Characterizing Human-Likeness in AI Generated Poetry: A Zero-shot Classification Study
A new arXiv preprint proposes a zero-shot classification pipeline to identify linguistic attributes that cause AI-generated poetry to be misclassified as human-written, aiming to improve detection robustness amid growing indistinguishability.
Jul 30, 2026
CARPRT: Class-Aware Zero-Shot Prompt Reweighting for Black-Box Vision-Language Models
Researchers introduced CARPRT, a class-aware prompt reweighting method for zero-shot image classification with black-box vision-language models, improving accuracy by modeling prompt-class dependencies without requiring model training or fine-tuning.
Jul 18, 2026
MASTE: A Multi-Agent Pipeline for Zero-Shot Aspect Sentiment Triplet Extraction
Researchers introduced MASTE, a multi-agent pipeline that improves zero-shot aspect sentiment triplet extraction by decomposing the task into sequential, specialized agent stages — enabling training-free performance that narrows the gap to supervised methods without labeled data.
Jul 10, 2026
I used Claude Fable 5 for zero-shot coding, and understood why Anthropic locked it down - XDA
A developer tested an unreleased, unannounced Anthropic model called 'Claude Fable 5' for zero-shot coding tasks and interpreted its performance as justifying Anthropic’s decision to restrict access — though no official model by that name exists in Anthropic’s public releases or documentation.
Jul 9, 2026
Evaluating Time Series Foundation Models for Electricity Price Forecasting: Contamination Risk, Distributional Shifts, and Covariate Dependence
Researchers introduce a two-dataset benchmarking framework to rigorously evaluate time series foundation models (TSFMs) for electricity price forecasting, revealing their competitive but context-dependent performance and identifying contamination risk and covariate dependence as critical evaluation challenges.
Jul 8, 2026
google/tabfm-1.0.0
Google Research released TabFM, a zero-shot foundation model for tabular data that claims to perform classification and regression without fine-tuning or hyperparameter search by treating training examples as context.
Published Jul 4, 2026 · Analyzed Jul 6, 2026
Selective Test-Time Debiasing for CLIP via Reward Gating
Researchers propose a new method to reduce bias in vision language models.
Published Jul 2, 2026 · Analyzed Jul 5, 2026