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arXiv:2602.22918v1 Announce Type: new Abstract: Vision-language models (VLMs) can read text from images, but where does this optical character recognition (OCR) information enter the language …
arXiv:2602.23057v1 Announce Type: new Abstract: Transformer attention is typically implemented using softmax normalization, which enforces attention weights with unit sum normalization. While effective in many …
arXiv:2602.23062v1 Announce Type: new Abstract: Case Report Forms (CRFs) collect data about patients and are at the core of well-established practices to conduct research in …
arXiv:2602.23071v1 Announce Type: new Abstract: While second language (L2) learners may acquire target syntactic word order, mapping this syntax onto appropriate prosodic structures remains a …
arXiv:2602.23075v1 Announce Type: new Abstract: Large language models (LLMs) have created new opportunities to enhance the efficiency of scholarly activities; however, challenges persist in the …
arXiv:2602.23079v1 Announce Type: new Abstract: The rapid advancement of large language models (LLMs) has enabled powerful authorship inference capabilities, raising growing concerns about unintended deanonymization …
arXiv:2602.23136v1 Announce Type: new Abstract: Multimodal LLMs can process speech and images, but they cannot hear a speaker's voice or see an object's texture. We …
arXiv:2602.23184v1 Announce Type: new Abstract: We present MTRAG-UN, a benchmark for exploring open challenges in multi-turn retrieval augmented generation, a popular use of large language …
arXiv:2602.23197v1 Announce Type: new Abstract: Transformer-based large language models exhibit in-context learning, enabling adaptation to downstream tasks via few-shot prompting with demonstrations. In practice, such …
arXiv:2602.23225v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) are often advertised as enabling parallel token generation, yet practical fast DLMs frequently converge to left-to-right, …
arXiv:2602.23266v1 Announce Type: new Abstract: Achieving human-like responsiveness is a critical yet challenging goal for cascaded spoken dialogue systems. Conventional ASR-LLM-TTS pipelines follow a strictly …
arXiv:2602.23286v1 Announce Type: new Abstract: Real-world Table-Text question answering (QA) tasks require models that can reason across long text and source tables, traversing multiple hops …
