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arXiv:2604.06195v1 Announce Type: new Abstract: Large language models often produce unsupported claims. We frame this as a misclassification error at the output boundary, where internally …
arXiv:2604.06542v1 Announce Type: new Abstract: Empirical scaling laws for language models have encouraged the development of ever-larger LLMs, despite their growing computational and memory costs. …
Law professor Andrew Guthrie Ferguson chats with Ars about his new book,Your Data Will Be Used Against You.
arXiv:2604.06502v1 Announce Type: new Abstract: Vision-Language Models (VLMs) face significant safety vulnerabilities from malicious prompt attacks due to weakened alignment during visual integration. Existing defenses …
arXiv:2604.06650v1 Announce Type: new Abstract: Existing prompt-based fine-tuning methods typically learn task-specific prompts independently, imposing significant computing and storage overhead at scale when deploying multiple …
arXiv:2604.06291v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning of Large Language Models (LLMs), and recent Mixture-of-Experts (MoE) extensions further enhance flexibility by …
Any tanker passing must reveal its cargo so Iran can determine transit fee amount.
arXiv:2604.06515v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) allows scaling of language and vision models efficiently by activating only a small subset of experts per …
arXiv:2604.06201v1 Announce Type: new Abstract: While most reading comprehension benchmarks for LLMs focus on factual information that can be answered by localizing specific textual evidence, …
Matei Zaharia has won the top honor from the Association for Computing Machinery. Now he's working on AI for research and says AGI is simply …
arXiv:2604.06464v1 Announce Type: new Abstract: Conformal prediction provides distribution-free prediction intervals with finite-sample coverage guarantees, and recent work by Snell \& Griffiths reframes it as …
arXiv:2604.06631v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training while preserving data privacy, but its practical deployment is hampered by system and …