ChatBCG: Can AI Read Your Slide Deck?
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Abstract (arXiv)
Multimodal models like GPT4o and Gemini Flash are exceptional at inference and summarization tasks, which approach human-level in performance. However, we find that these models underperform compared to humans when asked to do very specific 'reading and estimation' tasks, particularly in the context of visual charts in business decks. This paper evaluates the accuracy of GPT 4o and Gemini Flash-1.5 in answering straightforward questions about data on labeled charts (where data is clearly annotated on the graphs), and unlabeled charts (where data is not clearly annotated and has to be inferred from the X and Y axis). We conclude that these models aren't currently capable of reading a deck accurately end-to-end if it contains any complex or unlabeled charts. Even if a user created a deck of only labeled charts, the model would only be able to read 7-8 out of 15 labeled charts perfectly end-to-end. For full list of slide deck figures visit https://www.repromptai.com/chat_bcg
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 6 references that are in the map. First posted on arXiv 16 Jul 2024.
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References · 5
- 2023A Survey on Evaluation of Large Language Modelscited by 1,227
- 2023UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoningcited by 158
- 2024Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Surveycited by 82
- 2023Evaluating Large Language Models on Graphs: Performance Insights and Comparative Analysiscited by 35
- 2023Enhanced Chart Understanding in Vision and Language Task via Cross-modal Pre-training on Plot Table Pairscited by 15
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