Researchers have investigated how vision-language models (VLMs) extract specific values from vertical bar charts, focusing on models like Qwen2.5VL-7B-Instruct and InternVL3.5-8B. Their analysis reveals that the top region of a bar, despite having fewer visual tokens, contributes more to answer preference than the bar's body. The study also found that information related to legends and series is processed earlier in the model layers, while geometric and scale states are handled in later layers. Interestingly, both models demonstrated an ability to use geometric and scale information from different sources, though Qwen showed greater sensitivity to context. AI
IMPACT Provides insight into the internal workings of VLMs for chart interpretation, potentially guiding future model development.
RANK_REASON Academic paper detailing research into VLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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