Khái niệm cốt lõi
The author explores the design aspects of Multimodal Small Language Models (MSLMs) and introduces Mipha, an efficient multimodal assistant that outperforms large models without additional training data.
Tóm tắt
The content discusses the challenges faced by Multimodal Large Language Models (MLLMs) due to high computational demands and introduces the concept of Multimodal Small Language Models (MSLMs). The development of Mipha, a new family of MSLMs, is presented as a solution that surpasses leading open-source MLLMs in performance across various benchmarks. The study emphasizes the importance of fine-tuning both visual and language components for effective MSLMs.
The analysis dissects key components like language models, visual representations, and optimization strategies in developing strong MSLMs. Insights reveal that freezing the language model can negatively impact performance, while full-parameter tuning and Low-Rank Adaptation (LoRA) are effective alternatives. Experiment results demonstrate that scaling image resolution is not always beneficial for MSLMs, highlighting the need for a balanced approach. Overall, the study provides valuable insights into optimizing efficient MSLMs.
Thống kê
InstructBLIP-8B: 85.3 on VQAv2; 41.0 on GQA; 19.6 on VizWiz; 61.0 on SQAI; 42.5 on VQAT
MoE-LLaVA-3.6B: 79.9 on POPE; 62.6 on ScienceQA; 43.7 on VQAT; 70.3 on MM-Vet; 57.0 on SEED-Bench-img
Trích dẫn
"Reducing computational demands of language model could decrease overall inference costs."
"Increasing image resolution is not always beneficial for model generalization."
"Fine-tuning both visual and language components is critical for successful MSLM implementation."