Papers by Charles Corbière
DRIVINGVQA: A Dataset for Interleaved Visual Chain-of-Thought in Real-World Driving Scenarios (2026.findings-eacl)
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| Challenge: | Chain-of-thought (CoT) prompting is a prompting strategy that improves reasoning in large language models, but its effectiveness in vision-language models remains limited due to over-reliance on textual cues and memorized knowledge. |
| Approach: | They propose a visual question-answering dataset derived from driving theory exams that incorporates textual explanations with visual tokens extracted from entities relevant to the reasoning process. |
| Outcome: | The proposed approach outperforms chain-of-thought prompting in large language models and vision-language models in real-world scenarios. |