The Role of Chain-of-Thought in Complex Vision-Language Reasoning Task
Yifan Wu Pengchuan Zhang Wenhan Xiong Barlas Oguz James C. Gee Yixin Nie
The study explores the effectiveness of the Chain-of-Thought approach, known
for its proficiency in language tasks by breaking them down into sub-tasks and
intermediate steps, in improving vision-language tasks that demand
sophisticated perception and reasoning. We present the "Description then
Decision" strategy, which is inspired by how humans process signals. This
strategy significantly improves probing task performance by 50%, establishing
the groundwork for future research on reasoning paradigms in complex
vision-language tasks.