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arXiv:2603.23146v1 Announce Type: new Abstract: The widespread adoption of Large Language Models (LLMs) has made the detection of AI-Generated text a pressing and complex challenge. …
arXiv:2603.23160v1 Announce Type: new Abstract: Benchmarking AI systems in multi-turn interactive scenarios is essential for understanding their practical capabilities in real-world applications. However, existing evaluation …
arXiv:2603.23172v1 Announce Type: new Abstract: Multilingual intent classification is central to customer-service systems on global logistics platforms, where models must process noisy user queries across …
arXiv:2603.23184v1 Announce Type: new Abstract: Reward modeling represents a long-standing challenge in reinforcement learning from human feedback (RLHF) for aligning language models. Current reward modeling …
arXiv:2603.23219v1 Announce Type: new Abstract: Amidst the rising capabilities of generative AI to mimic specific human styles, this study investigates the ability of state-of-the-art large …
arXiv:2603.23229v1 Announce Type: new Abstract: Internet memes represent a popular form of multimodal online communication and often use figurative elements to convey layered meaning through …
arXiv:2603.23251v1 Announce Type: new Abstract: The advancing fluency of LLMs raises important questions about their ability to emulate complex human traits, including emotional expression and …
arXiv:2603.22292v1 Announce Type: new Abstract: Sequential decision making using Markov Decision Process underpins many realworld applications. Both model-based and model free methods have achieved strong …
arXiv:2603.22294v1 Announce Type: new Abstract: Synthetic Data Generation (SDG), leveraging Large Language Models (LLMs), has recently been recognized and broadly adopted as an effective approach …
arXiv:2603.22299v1 Announce Type: new Abstract: Large language models (LLMs) are often confidently wrong, making reliable uncertainty estimation (UE) essential. Output-based heuristics are cheap but brittle, …
arXiv:2603.22300v1 Announce Type: new Abstract: Scaling Transformers to ultra-long contexts is bottlenecked by the $O(n^2 d)$ cost of self-attention. Existing methods reduce this cost along …
arXiv:2603.22301v1 Announce Type: new Abstract: Large Language Models (LLMs) perform internal computations in continuous vector spaces yet produce discrete tokens -- a fundamental mismatch whose …