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arXiv:2603.16292v1 Announce Type: new Abstract: Spoken Question Answering (Spoken QA) presents a challenging cross-modal problem: effectively aligning acoustic queries with textual knowledge while avoiding the …
arXiv:2603.16299v1 Announce Type: new Abstract: We introduce PyPhonPlan, a Python toolkit for implementing dynamical models of phonetic planning using coupled dynamic neural fields and task …
arXiv:2603.16354v1 Announce Type: new Abstract: We present PashtoCorp, a 1.25-billion-word corpus for Pashto, a language spoken by 60 million people that remains severely underrepresented in …
arXiv:2603.16406v1 Announce Type: new Abstract: This paper evaluates current Large Language Model (LLM) benchmarking for Icelandic, identifies problems, and calls for improved evaluation methods in …
arXiv:2603.16410v1 Announce Type: new Abstract: Creative plot generation presents a fundamental challenge for language models: transforming a concise premise into a coherent narrative that sustains …
arXiv:2603.16411v1 Announce Type: new Abstract: Entity recognition in Automatic Speech Recognition (ASR) is challenging for rare and domain-specific terms. In domains such as finance, medicine, …
arXiv:2603.16415v1 Announce Type: new Abstract: Multi-hop question answering (QA) requires reasoning across multiple documents, yet existing retrieval-augmented generation (RAG) approaches address this either through graph-based …
arXiv:2603.16435v1 Announce Type: new Abstract: The growing context length of Large Language Models (LLMs) enlarges the Key-Value (KV) cache, limiting deployment in resource-limited environments. Prior …
arXiv:2603.16459v1 Announce Type: new Abstract: Diffusion large language models (D-LLMs) have emerged as a promising alternative to auto-regressive models due to their iterative refinement capabilities. …
arXiv:2603.16483v1 Announce Type: new Abstract: Emotion is a core paralinguistic feature in voice interaction. It is widely believed that emotion understanding models learn fundamental representations …
arXiv:2603.16496v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external memory to support long-horizon interaction, personalized assistance, and multi-step reasoning. However, …
arXiv:2603.16544v1 Announce Type: new Abstract: Large language models (LLMs) often rely on outdated knowledge when answering time-sensitive questions, leading to confident yet incorrect responses. Without …