Papers by Ritambhara Singh
What Do VLMs NOTICE? A Mechanistic Interpretability Pipeline for Gaussian-Noise-free Text-Image Corruption and Evaluation (2025.naacl-long)
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| Challenge: | Vision-Language Models (VLMs) have gained prominence due to their success in solving complex cross-modal tasks. |
| Approach: | They propose a Gaussian-Noise-free pipeline for mechanistic interpretability in VLMs that introduces Semantic Image Pairs corruption, the first visual counterpart to Symmetric Token Replacement for text. |
| Outcome: | The proposed pipeline identifies a set of “universal attention heads” in BLIP and LLaVA that consistently contribute across different tasks and modalities. |
UbuntuGuard: A Culturally-Grounded Policy Benchmark for Equitable AI Safety in African Languages. (2026.findings-acl)
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Tassallah Abdullahi, Macton Mgonzo, Mardiyyah Oduwole, Paul Okewunmi, Abraham Toluwase Owodunni, Ritambhara Singh, Carsten Eickhoff
| Challenge: | Current guardian models are predominantly Western-centric and optimized for high-resource languages . low-resourced African languages are vulnerable to evolving harms, cross-lingual failures, cultural misalignment . |
| Approach: | They propose a policy-based safety benchmark for African languages built from adversarial queries authored by 155 domain experts across sensitive fields. |
| Outcome: | The proposed model overestimates multilingual safety, cross-lingual transfer provides partial but insufficient coverage, and dynamic models struggle to localize African-language contexts. |
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 . |
Mechanisms of Prompt-Induced Hallucination in Vision–Language Models (2026.acl-long)
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William Rudman, Michal Golovanevsky, Dana Arad, Yonatan Belinkov, Carsten Eickhoff, Ritambhara Singh, Kyle Mahowald
| Challenge: | Large vision–language models (VLMs) often hallucinate by favoring textual prompts over visual evidence. |
| Approach: | They study the failure mode of large vision–language models by focusing on textual prompts over visual evidence. |
| Outcome: | The proposed model overestimates the number of objects in an image . it hallucinates additional waterlilies when asked to describe a mismatched number of items . the model ablation reduces prompt-induced hallucinosities by at least 40% without additional training . |
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%. |