This paper investigates how the specificity of natural language prompts influences zero-shot classification performance in modern vision language models (VLMs) under severe data scarcity. Using a curated 285 image subset of MS COCO containing three everyday postures (sitting, standing, and walking/running), we evaluate OpenCLIP, MetaCLIP2, and SigLIP alongside unimodal and pose-based baselines. We introduce a three tier prompt design, minimal labels, action cues, and compact geometric descriptions and systematically vary only the linguistic detail. Our results reveal a counterintuitive trend where simpler prompts consistently outperform more detailed ones, a phenomenon we term prompt overfitting. Grad-CAM attribution further shows that prompt specificity shifts attention between contextual and pose-relevant regions, explaining the model dependent behaviour. The study provides a controlled analysis of prompt granularity in low resource image based posture recognition, highlights the need for careful prompt design when labels are scarce.

We introduce DateLogicQA, a human-curated benchmark of 190 questions specifically designed to understand temporal bias in Large Language Models (LLMs). Covering seven date formats across past, present, and future contexts, DateLogicQA examines four reasoning types: commonsense, factual, conceptual, and numerical. Through human-led evaluations of 12 state-of-the-art LLMs, we identify Representation-Level Bias, arising from suboptimal embeddings that distort date semantics, and Logical-Level Bias, manifesting when correct date tokens yield flawed temporal reasoning. Our findings underscore persistent challenges in handling various date formats and temporal contexts, revealing the need for more robust pretraining data, targeted post-training methods, and precise tokenization strategies. By illuminating these biases, we provide actionable insights to guide the development of LLMs for accurate temporal reasoning across diverse real-world applications.