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arXiv:2602.13680v1 Announce Type: new Abstract: Large Language Models (LLMs) encounter significant performance bottlenecks in long-sequence tasks due to the computational complexity and memory overhead inherent …
arXiv:2602.13691v1 Announce Type: new Abstract: Recent advancements in Large Language Model (LLM) agents have demonstrated strong capabilities in executing complex tasks through tool use. However, …
arXiv:2602.13263v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems often degrade on accented speech because acoustic-phonetic and prosodic shifts induce a mismatch to training …
arXiv:2602.13452v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly proposed for crisis preparedness and response, particularly for multilingual communication. However, their suitability for …
arXiv:2602.13455v1 Announce Type: new Abstract: The rise of digital technology has dramatically increased the potential for cyberbullying and online abuse, necessitating enhanced measures for detection …
arXiv:2602.13466v1 Announce Type: new Abstract: The ability of machine learning models to store input information in hidden layer vector embeddings, analogous to the concept of …
arXiv:2602.13504v1 Announce Type: new Abstract: The rapid integration of large language models into newsroom workflows has raised urgent questions about the prevalence of AI-generated content …
arXiv:2602.13517v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated impressive reasoning capabilities by scaling test-time compute via long Chain-of-Thought (CoT). However, recent findings …
arXiv:2602.13540v1 Announce Type: new Abstract: Large language models (LLMs) are widely deployed as general-purpose problem solvers, making accurate confidence estimation critical for reliable use. Prior …
arXiv:2602.13551v1 Announce Type: new Abstract: Reward models (RMs) play a central role throughout the language model (LM) pipeline, particularly in non-verifiable domains. However, the dominant …
arXiv:2602.13567v1 Announce Type: new Abstract: Standard Knowledge Distillation (KD) compresses Large Language Models (LLMs) by optimizing final outputs, yet it typically treats the teacher's intermediate …
arXiv:2602.13571v1 Announce Type: new Abstract: Large language models (LLMs) have revolutionized natural language processing, yet hallucinations in knowledge-intensive tasks remain a critical challenge. Retrieval-augmented generation …