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arXiv:2603.23534v1 Announce Type: new Abstract: This paper describes my submission to the Polarization Shared Task at SemEval-2025, which addresses polarization detection and classification in social …
arXiv:2603.23624v1 Announce Type: new Abstract: Digging-in effects, where disambiguation difficulty increases with longer ambiguous regions, have been cited as evidence for self-organized sentence processing, in …
arXiv:2603.23646v1 Announce Type: new Abstract: While recent work has benchmarked large language models on Swiss legal translation (Niklaus et al., 2025) and academic legal reasoning …
arXiv:2603.23659v1 Announce Type: new Abstract: When large language models make ethical judgments, do their internal representations distinguish between normative frameworks, or collapse ethics into a …
arXiv:2603.23678v1 Announce Type: new Abstract: Large Language Models (LLMs) offer transformative solutions across many domains, but healthcare integration is hindered by strict data privacy constraints. …
arXiv:2603.23701v1 Announce Type: new Abstract: In Large Language Model (LLM) inference, early-exit refers to stopping computation at an intermediate layer once the prediction is sufficiently …
arXiv:2603.23750v1 Announce Type: new Abstract: Large language models are increasingly consulted for Islamic knowledge, yet no comprehensive benchmark evaluates their performance across core Islamic disciplines. …
arXiv:2603.23797v1 Announce Type: new Abstract: Children in many parts of the world hear relatively little speech directed to them, yet still reach major language development …
arXiv:2603.23821v1 Announce Type: new Abstract: Linguistic representation learning in deep neural language models (LMs) has been studied for decades, for both practical and theoretical reasons. …
arXiv:2603.23841v1 Announce Type: new Abstract: While Large Language Models (LLMs) are increasingly used as primary sources of information, their potential for political bias may impact …
arXiv:2603.23844v1 Announce Type: new Abstract: Recent work shows overwhelming evidence that LLMs, even those trained to scale their reasoning trace, perform unsatisfactorily when solving planning …
arXiv:2603.23848v1 Announce Type: new Abstract: LLMs are increasingly used as long-running conversational agents, yet every major benchmark evaluating their memory treats user information as static …
