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arXiv:2602.17949v1 Announce Type: new Abstract: Background: Clinical named entity recognition tools commonly map free text to Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs). …
arXiv:2602.17981v1 Announce Type: new Abstract: Retrieval-augmented generation is increasingly used for financial question answering over long regulatory filings, yet reliability depends on retrieving the exact …
arXiv:2602.18029v1 Announce Type: new Abstract: Evaluation is no longer a final checkpoint in the machine learning lifecycle. As AI systems evolve from static models to …
arXiv:2602.18092v1 Announce Type: new Abstract: Conversational AI has been proposed as a scalable way to correct public misconceptions and spread misinformation. Yet its effectiveness may …
arXiv:2602.18137v1 Announce Type: new Abstract: Large Language Models (LLMs), despite extensive pretraining on broad internet corpora, often struggle to adapt effectively to specialized domains. There …
arXiv:2602.18145v1 Announce Type: new Abstract: Hallucination detection is critical for ensuring the reliability of large language models (LLMs) in context-based generation. Prior work has explored …
arXiv:2602.18152v1 Announce Type: new Abstract: Large language models generate text through probabilistic sampling from high-dimensional distributions, yet how this process reshapes the structural statistical organization …
arXiv:2602.18154v1 Announce Type: new Abstract: Jailbreaking poses a significant risk to the deployment of Large Language Models (LLMs) and Vision Language Models (VLMs). VLMs are …
arXiv:2602.18171v1 Announce Type: new Abstract: Clickbait headlines degrade the quality of online information and undermine user trust. We present a hybrid approach to clickbait detection …
arXiv:2602.18176v1 Announce Type: new Abstract: Masked Diffusion Models (MDMs) offer greater flexibility in decoding order than autoregressive models but require careful planning to achieve high-quality …
arXiv:2602.18217v1 Announce Type: new Abstract: Real-time sentence comprehension imposes a significant load on working memory, as comprehenders must maintain contextual information to anticipate future input. …
arXiv:2602.18232v1 Announce Type: new Abstract: Recent work on test-time scaling for large language model (LLM) reasoning typically assumes that allocating more inference-time computation uniformly improves …