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arXiv:2603.03316v1 Announce Type: cross Abstract: Most sign language recognition research relies on Transfer Learning (TL) from vision-based datasets such as ImageNet. Some extend this to …
arXiv:2603.03318v1 Announce Type: cross Abstract: Recent advances in Natural Language Processing have been predominantly driven by transformer-based architectures, which rely heavily on self-attention mechanisms to …
arXiv:2603.03319v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used as scalable evaluators of model outputs, but their preference judgments exhibit systematic biases …
arXiv:2603.03320v1 Announce Type: cross Abstract: Effective communication often relies on aligning a message with an audience's narrative and worldview. Narrative shift involves transforming text to …
arXiv:2603.03321v1 Announce Type: cross Abstract: Evaluating instruction following in Large Language Models requires decomposing instructions into verifiable requirements and assessing satisfaction--tasks currently dependent on manual …
arXiv:2603.03322v1 Announce Type: cross Abstract: Recent advancements in Large Language Model (LLM) agents have demonstrated remarkable potential in automatic knowledge discovery. However, rigorously evaluating an …
arXiv:2603.03323v1 Announce Type: cross Abstract: Large language models (LLMs) aligned for safety often suffer from over-refusal, the tendency to reject seemingly toxic or benign prompts …
arXiv:2603.03324v1 Announce Type: cross Abstract: Controlling stylistic attributes in large language models (LLMs) remains challenging, with existing approaches relying on either prompt engineering or post-training …
arXiv:2603.03325v1 Announce Type: cross Abstract: Large language models (LLMs) have become integral to modern Human-AI collaboration workflows, where accurately understanding user intent serves as a …
arXiv:2603.03326v1 Announce Type: cross Abstract: Aligning Large Language Models (LLMs) with specific personas typically relies on expensive and monolithic Supervised Fine-Tuning (SFT) or RLHF. While …
arXiv:2603.03327v1 Announce Type: cross Abstract: User satisfaction is closely related to enterprises, as it not only directly reflects users' subjective evaluation of service quality or …
arXiv:2603.03293v1 Announce Type: new Abstract: Retrieval augmented generation (RAG) reduces hallucinations and factual errors in large language models (LLMs) by conditioning generation on retrieved external …