Academic
arXiv:2602.17431v1 Announce Type: new Abstract: Uncertainty quantification has emerged as an effective approach to closed-book hallucination detection for LLMs, but existing methods are largely designed …
arXiv:2602.17443v1 Announce Type: new Abstract: Evaluating the strategic reasoning capabilities of Large Language Models (LLMs) requires moving beyond static benchmarks to dynamic, multi-turn interactions. We …
arXiv:2602.17445v1 Announce Type: new Abstract: Multiple-choice question (MCQ) benchmarks have been a standard evaluation practice for measuring LLMs' ability to reason and answer knowledge-based questions. …
arXiv:2602.17465v1 Announce Type: new Abstract: Modern language models (LMs) increasingly require two critical resources: computational resources and data resources. Data selection techniques can effectively reduce …
arXiv:2602.17467v1 Announce Type: new Abstract: The increasing volume of hate speech on online platforms poses significant societal challenges. While the Natural Language Processing community has …
arXiv:2602.17469v1 Announce Type: new Abstract: The core theme of bidirectional alignment is ensuring that AI systems accurately understand human intent and that humans can trust …
arXiv:2602.17475v1 Announce Type: new Abstract: Large Language Models (LLMs) consistently excel in diverse medical Natural Language Processing (NLP) tasks, yet their substantial computational requirements often …
arXiv:2602.17513v1 Announce Type: new Abstract: Clinical free-text notes contain vital patient information. They are structured into labelled sections; recognizing these sections has been shown to …
arXiv:2602.17542v1 Announce Type: new Abstract: Fine-grained skill representations, commonly referred to as knowledge components (KCs), are fundamental to many approaches in student modeling and learning …
arXiv:2602.17546v1 Announce Type: new Abstract: Instruction-following language models are trained to be helpful and safe, yet their safety behavior can deteriorate under benign fine-tuning and …
arXiv:2602.17588v1 Announce Type: new Abstract: Despite rapid progress in autonomous web agents, human involvement remains essential for shaping preferences and correcting agent behavior as tasks …
arXiv:2602.17598v1 Announce Type: new Abstract: Current speech LLMs largely perform implicit ASR: on tasks solvable from a transcript, they are behaviorally and mechanistically equivalent to …
