Task-agnostic model pre-training is now the norm in Natural Language Processing, driven by the recent revolution in large language models (LLMs) like ChatGPT. These models showcase proficiency in tackling intricate reasoning tasks, adhering to instructions, and serving as the backbone for widely used AI assistants. Their success is attributed to a consistent enhancement in performance…

In the constantly evolving field of machine learning, particularly in semantic segmentation, the accurate estimation and validation of uncertainty have become increasingly vital. Despite numerous studies claiming advances in uncertainty methods, there remains a disconnection between theoretical development and practical application. Fundamental questions linger, such as whether it is feasible to separate data-related (aleatoric) and…

The practical deployment of multi-billion parameter neural rankers in real-world systems poses a significant challenge in information retrieval (IR). These advanced neural rankers demonstrate high effectiveness but are hampered by their substantial computational requirements for inference, making them impractical for production use. This dilemma poses a critical problem in IR, as it is necessary to…