Papers by Michael A. Lepori
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 . |
Racing Thoughts: Explaining Contextualization Errors in Large Language Models (2025.naacl-long)
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| Challenge: | Large Language Models have demonstrated a remarkable capacity for accomplishing a wide variety of language generation and classification tasks. |
| Approach: | They propose a race conditions hypothesis to explain contextualization errors . they propose to use a variety of techniques to test the hypothesis . |
| Outcome: | The proposed model fails to properly contextualize a financial institution if it does not include a bank . the proposed model is based on the race conditions hypothesis . |
Language Models Struggle to Use Representations Learned In-Context (2026.acl-long)
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| Challenge: | a recent study shows that large language models are capable of inducing rich representations of data that are seen in-context . a novel task, adaptive world modeling, shows that even the most performant LLMs cannot reliably leverage novel semantics defined in-constitut. |
| Approach: | They propose to use in-context representations to induce rich representations of data . they also propose to probe models using a novel task to enable flexible deployment . |
| Outcome: | The proposed model can use in-context representations to complete simple downstream tasks. |