4 accepted papers
Generating high-quality long-text remains challenging for Large Language Models (LLMs), as conventional supervised fine-tuning fails to ensure overall quality due to its teacher-forcing nature. Kahneman-Tversky Optimization (KTO), as a model alignment method that can holistically optimize generation
Mathematical reasoning remains an ongoing challenge for AI models, especially for geometry problems, which require both linguistic and visual signals. As the vision encoders of most MLLMs are trained on natural scenes, they often struggle to understand geometric diagrams, performing no better in geo…
In handling complex, domain-specific tasks, particularly in the context of state-owned assets and enterprises (SOAEs), general LLMs suffer from the knowledge gap due to insufficient exposure to domain-specific corpora, and the value disagreement, as they are aligned with universal values rather than…
Combining face-swapping with lip synchronization offers a cost-effective solution for generating customized talking faces. However, directly cascading existing models can introduce significant interference and reduce video clarity due to limited interaction space in the low-level RGB domain. To solv…