This paper presents a novel approach to geolocalization, a task that aims to predict the latitude and longitude of an image based on its visual content. Traditional methods in this domain often rely on databases, complex pipelines or large-scale image classification networks. In contrast, we propose a direct regression approach that simplifies the process by predicting the geographic coordinates directly from the image features. We leverage a pre-trained Vision Transformer(ViT) model, specifically the ViT of a pre-trained CLIP model, for feature extraction and introduce a regression head for coordinate prediction. Various configurations, including pre-training and task-specific adaptations, are tested and evaluated resulting in our model called ReGeo. Experimental results show that ReGeo offers competitive performance compared to the existing approaches, despite being simpler and with minimal supporting code. ReGeo consistently achieves the best performance at the continent level across the GWS15K, IM2GPS3K, and IM2GPS datasets, and even outperforms GeoCLIP at the country level on GWS15K. Notably, GWS15K exhibits a significant distribution shift compared to ReGeo's training data, highlighting the model's strong generalization capabilities. On the holdout dataset, ReGeo localizes 32 % of 5,000 images at the city level, and achieves increasingly higher accuracy at broader radii—88.08% at the regional level, 96.86 % at the country level, and 99.34 % at the continent level-demonstrating its effectiveness for mid-range to coarse-grained geo-localization tasks.
Despite the proliferation of Responsible Artificial Intelligence (RAI) principles, organizations struggle to translate them into practical implementation. This study investigates the challenges Swiss organizations face in implementing RAI through qualitative interviews with industry professionals and academic experts, complemented by a multi-stakeholder workshop. We first identify five critical pain points hampering RAI implementation: economic constraints, structural and procedural barriers, conceptual and technical challenges, cultural and behavioral resistance, and regulatory uncertainty. Then we propose the Control-Tangibility Framework, a novel framework that maps pain points along two fundamental dimensions: organizational control and challenge tangibility. Our framework provides organizations with a structured methodology to prioritize RAI efforts by considering both their ability to influence change and their capacity to observe aspects of the challenges. Furthermore, we provide practical insights for developing targeted implementation strategies that bridge the gap between ethical principles and operational practices. Our findings suggest that successful RAI implementation requires moving beyond compliance-focused approaches toward a comprehensive organizational transformation, supported by systematic assessment and prioritization of implementation challenges.
Large Language Models (LLMs) have demonstrated capabilities beyond basic text generation, like question answering, translation, and even stylistic text generation. Since these models are available for public use, enormous effort has been put into safety engineering to ensure that undesirable and harmful text is not generated and the generations are polite and empathetic. In this work, we examine the inherent empathy capabilities of five open-source LLMs and evaluate them from multiple angles using automated metrics to understand their capabilities and limitations. In the context of this work, “inherent” refers to the LLM's ability to generate empathetic text without having to explicitly prompt for it. We examine if empathy is treated as a style change or is the model demonstrating some understanding of the specific user's context. We find that LLMs use more emotion words than humans in their generations. They can also infer the user's emotional state, a crucial characteristic of empathy. Due to the probabilistic nature of obtaining generations, there is a tendency for the responses to drift away from the user's actual intent. In such cases, specific prompting allows the model to respond appropriately. We summarize the differences observed between human and LLM generations and conclude with a potential research direction for empathetic dialog generation that leverages the capabilities of LLMs.
Automated Essay Scoring (AES) is a challenging task requiring models to evaluate writing quality with human-like consistency while being computationally efficient and scalable. Current approaches often rely on computationally intensive prompt engineering or resource-heavy transformer models, limiting their practical application. In this study, we present a novel prompt-free approach using SetFit for AES that achieves competitive accuracy while significantly reducing computational overhead. Unlike traditional transformer- based models such as DeBERTa, SetFit enables sentence transformer fine-tuning with contrastive learning, making it suitable for essay scoring even in low-data regimes. To handle full-length essays, we extended Longformer into a custom sentence transformer with a 4096-token context window, overcoming the 512-token limitation of standard transformer architectures. Our fine-tuned model, published on Hugging Face, has gained over 6,000 downloads, indicating strong community interest in efficient, prompt-free approaches. Our results demonstrate that SetFit with an extended Longformer sentence transformer achieves competitive performance, making it a viable alternative to computationally expensive models. Beyond essay scoring, our approach shows promise for other long-form text analysis tasks such as legal document review, research paper assessment, and content quality evaluation in educational contexts. This work contributes to the growing exploration of efficient NLP methods for educational assessment, offering a practical alternative to resource-intensive prompt-based solutions.
In the era of large language models (LLMs), the detection of software vulnerabilities in code snippets remains a critical challenge, particularly in resource-constrained environments. This paper explores the efficacy of fine-tuned and zero-shot LLMs, and traditional machine learning (ML) models for classifying code snippets as "vulnerable" or "not vulnerable" based on the Software Common Weakness Enumeration (CWE) framework. We perform an evaluation of three fine-tuned LLMs: Flan-T5-small, Llama-3.2-1B-Instruct and Deepseek-llm7B-Base-alongside three traditional ML models: Logistic Regression, Multinomial Naive Bayes, and Linear Support Vector Classifier. We then compare these approaches with zero-shot models - Deepseek-R1 and ChatGPT-o1. Our experiments show that LLMs achieve competitive validation and test accuracies, with Llama-3.2-1B-Instruct and Deepseek-llm-7B-Base both achieving 84% accuracy on the test set. However, traditional ML models, particularly Logistic Regression and Linear SVC, demonstrate strong performance with 78% accuracy, highlighting their viability in scenarios where computational resources are limited. Furthermore, we address the challenges of dataset imbalance, resource-intensive fine-tuning, and the practical applicability of these models in real-world vulnerability detection pipelines. Our study emphasizes the importance of considering traditional ML models as viable alternatives to LLMs, especially in resource-constrained environments while contributing to the ongoing research on the practical applicability of AI, and data science in software security.
