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arXiv:2603.11412v1 Announce Type: new Abstract: Under surprisal theory, linguistic representations affect processing difficulty only through the bottleneck of surprisal. Our best estimates of surprisal come …
arXiv:2603.11736v1 Announce Type: new Abstract: In recent years, generative artificial intelligence (GenAI) systems have assumed increasingly crucial roles in selection processes, personnel recruitment and analysis …
arXiv:2603.11709v1 Announce Type: new Abstract: While scaling laws for Large Language Models (LLMs) have been extensively studied along dimensions of model parameters, training data, and …
arXiv:2603.11767v1 Announce Type: new Abstract: This paper presents an in-depth analysis of Wikidata qualifiers, focusing on their semantics and actual usage, with the aim of …
arXiv:2603.11414v1 Announce Type: new Abstract: We present MaterialFigBench, a benchmark dataset designed to evaluate the ability of multimodal large language models (LLMs) to solve university-level …
arXiv:2603.11053v1 Announce Type: new Abstract: Speculative decoding is a technique that uses multiple language models to accelerate infer- ence. Previous works have used an experi- …
arXiv:2603.11281v1 Announce Type: new Abstract: Medical question-answering benchmarks predominantly evaluate single-turn exchanges, failing to capture the iterative, clarification-seeking nature of real patient consultations. We introduce …
arXiv:2603.11239v1 Announce Type: new Abstract: The dynamic evolution of real-world necessitates model editing within Large Language Models. While existing methods explore modular isolation or parameter-efficient …
arXiv:2603.11178v1 Announce Type: new Abstract: Standard LLM distillation wastes compute on two fronts: problems the student has already mastered (near-zero gradients) and problems far beyond …
arXiv:2603.11277v1 Announce Type: new Abstract: The rapid proliferation of large language model (LLM)-based agentic systems raises critical concerns regarding digital sovereignty, environmental sustainability, regulatory compliance, …
arXiv:2603.11808v1 Announce Type: new Abstract: The transition from monolithic large language models (LLMs) to modular, skill-equipped agents represents a fundamental architectural shift in artificial intelligence …
arXiv:2603.11342v1 Announce Type: new Abstract: The study of the attribution of input features to the output of neural network models is an active area of …