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arXiv:2603.18171v1 Announce Type: new Abstract: Large language models (LLMs) achieve impressive results in terms of fluency in text generation, yet the nature of their linguistic …
arXiv:2603.18173v1 Announce Type: new Abstract: Large language models (LLMs) are largely motivated by their performance on popular topics and benchmarks at the time of their …
arXiv:2603.18184v1 Announce Type: new Abstract: Interlinear glossed text (IGT) is a standard notation for language documentation which is linguistically rich but laborious to produce manually. …
arXiv:2603.18203v1 Announce Type: new Abstract: The dominant paradigms of artificial intelligence were shaped by learning theories from psychology: behaviorism inspired reinforcement learning, cognitivism gave rise …
arXiv:2603.18358v1 Announce Type: new Abstract: Outliers in dynamic topic modeling are typically treated as noise, yet we show that some can serve as early signals …
arXiv:2603.18361v1 Announce Type: new Abstract: Conversational agents are required to respond to their users not only with high quality (i.e. commonsense bearing) responses, but also …
arXiv:2603.18363v1 Announce Type: new Abstract: Unsupervised Reinforcement Learning from Internal Feedback (RLIF) has emerged as a promising paradigm for eliciting the latent capabilities of Large …
arXiv:2603.18390v1 Announce Type: new Abstract: Corporate recruiters often need to screen many resumes within a limited time, which increases their burden and may cause suitable …
arXiv:2603.18409v1 Announce Type: new Abstract: Current document chunking methods for Retrieval-Augmented Generation (RAG) typically linearize text. This forced linearization strips away intrinsic topological hierarchies, creating …
arXiv:2603.18411v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong reasoning capabilities but typically require expensive post-training to reach high performance. Recent test-time alignment …
arXiv:2603.18425v1 Announce Type: new Abstract: Task interference, the performance degradation caused by task switches within a single conversation, has been studied exclusively in text-only settings …
arXiv:2603.18428v1 Announce Type: new Abstract: Decoding strategies largely determine the quality of Large Language Model (LLM) outputs, yet widely used heuristics such as greedy or …