This research investigates the effect of instruction-tuning on the similarity between large language models (LLMs) and human language processing. The study examines brain alignment (the similarity of LLM internal representations…
Continue readingThe paper discusses the challenges of extending large-scale pretraining and instruction tuning to vision-language models due to the diversity in visual inputs. The authors propose a more efficient method for…
Continue readingThe paper presents a new approach to visual instruction tuning by introducing a fine-grained visual instruction dataset, LVIS-INSTRUCT 4V, containing 220K visually aligned and context-aware instructions. The instructions are produced…
Continue readingThe study focuses on improving large multimodal models (LMMs), specifically the LLaVA model, through visual instruction tuning. By using CLIP-ViT-L-336px with an MLP projection and adding academic-task-oriented VQA data, the…
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