Advancements in Vision and Language Research Driven by Large Language Models

In recent years, there has been significant progress in the field of vision and language research, thanks to advancements in key methodological approaches and larger datasets. Large language models (LLMs) have demonstrated impressive emergent abilities across various domains, including mathematical reasoning, diagram comprehension, and multimodal tasks. However, these models have limitations, such as their inability to perform OCR-free mathematical reasoning and control when pushed to their limits.
To overcome these challenges, researchers adopted a broad capability overview approach instead of conducting a systematic study with 100 requests per day. This allowed them to provide insights into the current state of MLLs despite rate limit restrictions. The article highlights notable MLLs that have shown promising potential, including PaLM-E, Flamingo, LLaVA-1.5, Instruct-BLIP, IDEFICS, Qwen, Kosmos-2, and recently, GPT-4V. These models have demonstrated OCR-free mathematical reasoning, diagram comprehension, and multimodal tasks.
The article also discusses the challenges faced by MLLs, such as their inability to perform OCR-free mathematical reasoning and control when pushed to their limits. To address these limitations, researchers are exploring new approaches that can widen the spectrum of achievable tasks by incorporating additional modalities, including visual information.
In summary, the article provides a comprehensive overview of the current state of MLLs, highlighting their impressive abilities and limitations. It also discusses potential solutions to overcome these challenges and explore new approaches for improving the capabilities of MLLs in the future.