4 accepted papers
Mixture-of-Experts (MoE) enables efficient scaling of large language models by activating only a subset of experts per input token. However, deploying MoE-based models incurs significant memory overhead due to the need to retain all experts in memory. While structured pruning is promising to reduce…
Data efficiency is crucial in domain-specific continual pre-training (CPT) of large language models (LLMs), especially under resource constraints. Aiming for “small data, big impact,” this work addresses the limitations of existing domain-specific data selection strategies, which often rely on scarc
Despite the rapid development of large language models (LLMs), existing benchmark datasets often focus on low-level cognitive tasks, such as factual recall and basic comprehension, while providing limited coverage of higher-level reasoning skills, including analysis, evaluation, and creation. In thi
Brain disorders have been consistently associated with abnormalities in specific brain regions or neural circuits. Identifying key brain regional activities and functional connectivity patterns is essential for discovering more precise neurobiological biomarkers. However, previous studies have prima…