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arXiv:2602.13110v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as judges to replace costly human preference labels in pairwise evaluation. Despite their …
arXiv:2602.13123v1 Announce Type: new Abstract: Living languages are shaped by a host of conflicting internal and external evolutionary pressures. While some of these pressures are …
arXiv:2602.13139v1 Announce Type: new Abstract: Language identification (LID) is an essential step in building high-quality multilingual datasets from web data. Existing LID tools (such as …
arXiv:2602.13194v1 Announce Type: new Abstract: The entropy rate of printed English is famously estimated to be about one bit per character, a benchmark that modern …
arXiv:2602.12286v1 Announce Type: cross Abstract: Fusing DNA foundation models with large language models (LLMs) for DNA-language reasoning raises a fundamental question: at what level should …
arXiv:2602.12301v1 Announce Type: cross Abstract: Although annotated music descriptor datasets for user queries are increasingly common, few consider the user's intent behind these descriptors, which …
arXiv:2602.12418v1 Announce Type: cross Abstract: Jailbreak attacks remain a persistent threat to large language model safety. We propose Context-Conditioned Delta Steering (CC-Delta), an SAE-based defense …
arXiv:2602.12526v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) has significantly enhanced the reasoning capabilities of Large Language Models (LLMs), especially when combined with reinforcement learning (RL) …
arXiv:2602.12528v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have inspired new paradigms for document reranking. While this paradigm better exploits the …
arXiv:2602.12546v1 Announce Type: cross Abstract: We present a decoder-only Conformer for automatic speech recognition (ASR) that processes speech and text in a single stack without …
arXiv:2602.12601v1 Announce Type: cross Abstract: Self-attention is often viewed as probabilistic query-key lookup, motivating designs that preserve normalized attention scores and fixed positional semantics. We …
arXiv:2602.12618v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) incur significant computational cost from processing numerous vision tokens through all LLM layers. Prior pruning …