Yuqian Fu

Recent efforts to accelerate inference in Multimodal Large Language Models (MLLMs) have largely focused on visual token compression. The effectiveness of these methods is commonly evaluated by measuring the accuracy drop on existing MLLM benchmarks before and after compression. However, these ben...

Instruction tuning is crucial for optimizing Large Language Models (LLMs), as the quality and diversity of instructional data significantly influence model performance. This naturally underscores the importance of an effective and efficient data selection strategy. However, recent mainstream data...