Language and Vision-Language Models exhibit impressive language capabilities akin to human reasoning. However, unlike humans who acquire language through embodied, interactive experiences, these models learn from static datasets without real-world interaction. This difference raises questions about how they conceptualize abstract notions and whether their reasoning aligns with human cognition. We investigate spatial conceptualizations of LLMs and VLMs by conducting analogy prompting studies with LLMs, VLMs, and human participants. We assess their ability to generate and interpret analogies for spatial concepts. We quantitatively compare the analogies produced by each group, examining the impact of multimodal inputs and reasoning mechanisms. Our findings indicate that generative models can produce and interpret analogies but differ significantly from human reasoning in their abstraction of spatial concepts - variability influenced by input modality, model size, and prompting methods, with analogy-based prompts not consistently enhancing alignment. Contributions include a methodology for probing generative models through analogies; a comparative analysis of analogical reasoning among models, and humans; and insights into the effect of multimodal inputs on reasoning.

This paper presents a federated learning system with differential privacy for hate speech detection, tailored to low-resource languages. By fine-tuning pre-trained language models, ALBERT emerged as the most effective option for balancing performance and privacy. Experiments demonstrated that federated learning with differential privacy performs adequately in low-resource settings, though datasets with fewer than 20 sentences per client struggled due to excessive noise. Balanced datasets and augmenting hateful data with non-hateful examples proved critical for improving model utility. These findings offer a scalable and privacy-conscious framework for integrating hate speech detection into social media platforms and browsers, safeguarding user privacy while addressing online harm.