Papers by Amir Bar
Pixels Versus Priors: Controlling Knowledge Priors in Vision-Language Models through Visual Counterfacts (2025.emnlp-main)
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Michal Golovanevsky, William Rudman, Michael A. Lepori, Amir Bar, Ritambhara Singh, Carsten Eickhoff
| Challenge: | Multimodal Large Language Models perform well on visual question answering tasks, but it remains unclear whether their reasoning relies more on memorized world knowledge or on visual information present in the input image. |
| Approach: | They propose a dataset of visual-realistic counterfactuals that put world knowledge priors into conflict with visual input. |
| Outcome: | The proposed dataset puts world knowledge priors into conflict with visual input . it shows that model predictions shift toward visual evidence in mid-to-late layers . |
DiaSet: An Annotated Dataset of Arabic Conversations (2024.lrec-main)
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Abraham Israeli, Aviv Naaman, Guy Maduel, Rawaa Makhoul, Dana Qaraeen, Amir Ejmail, Dina Lisnanskey, Julian Jubran, Shai Fine, Kfir Bar
| Challenge: | DiaSet is a dataset of dialectical Arabic speech manually transcribed and annotated for two downstream tasks. |
| Approach: | They propose to manually transcribe and annotate Arabic speech for sentiment analysis and named entity recognition. |
| Outcome: | The proposed dataset encapsulates the Palestine dialect, predominantly spoken in Palestine, Israel, and Jordan. |
Forgotten Polygons: Multimodal Large Language Models are Shape-Blind (2025.findings-acl)
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William Rudman, Michal Golovanevsky, Amir Bar, Vedant Palit, Yann LeCun, Carsten Eickhoff, Ritambhara Singh
| Challenge: | Multimodal Large Language Models struggle with visual reasoning, despite strong performance on vision-language tasks. |
| Approach: | They propose a visually cued chain-of-thought prompting that enhances multi-step mathematical reasoning by explicitly referencing visual annotations in diagrams. |
| Outcome: | The proposed model improves GPT-4o's accuracy on an irregular polygon side-counting task from 7% to 93%. |