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arXiv:2602.15436v1 Announce Type: new Abstract: Digitized historical archives make it possible to study everyday social life on a large scale, but the information extracted directly …
arXiv:2602.15449v1 Announce Type: new Abstract: Large Language Models (LLMs) are changing the coding paradigm, known as vibe coding, yet synthesizing algorithmically sophisticated and robust code …
arXiv:2602.15456v1 Announce Type: new Abstract: Agents based on Large Language Models (LLMs) are increasingly being deployed as interfaces to information on online platforms. These agents …
arXiv:2602.15504v1 Announce Type: new Abstract: Patients' expectations towards their treatment have a substantial effect on the treatments' success. While primarily studied in clinical settings, online …
arXiv:2602.15506v1 Announce Type: new Abstract: We introduce LuxMT, a machine translation system based on Gemma 3 27B and fine-tuned for translation from Luxembourgish (LB) into …
arXiv:2602.15509v1 Announce Type: new Abstract: The tendency for hallucination in current large language models (LLMs) negatively impacts dialogue systems. Such hallucinations produce factually incorrect responses …
arXiv:2602.15514v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly prevalent, reliable methods for detecting AI-generated text are critical for mitigating potential risks. …
arXiv:2602.15521v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) effectively scales model capacity while preserving computational efficiency through sparse expert activation. However, training high-quality MoEs from scratch …
arXiv:2602.15537v1 Announce Type: new Abstract: Pure speech language models aim to learn language directly from raw audio without textual resources. A key challenge is that …
arXiv:2602.15540v1 Announce Type: new Abstract: This paper introduces Perspectives, an interactive extension of the Discourse Analysis Tool Suite designed to empower Digital Humanities (DH) scholars …
arXiv:2602.15547v1 Announce Type: new Abstract: Text embedding models are widely used for semantic similarity tasks, including information retrieval, clustering, and classification. General-purpose models are typically …
arXiv:2602.15564v1 Announce Type: new Abstract: Text-to-SQL has recently achieved impressive progress, yet remains difficult to apply effectively in real-world scenarios. This gap stems from the …
