Papers by Mohamed Hendy

2 papers
Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs (2026.acl-long)

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Challenge: Current sycophancy research has largely overlooked its specific manifestations in the video-language domain.
Approach: They propose a video-LLM sycophancy benchmarking and evaluation to evaluate scophancies in video-LLMs.
Outcome: The proposed benchmark evaluates sycophantic behavior in state-of-the-art Video-LLMs across diverse question formats, prompt biases, and visual reasoning tasks.
Domain Specific Sub-network for Multi-Domain Neural Machine Translation (2022.aacl-short)

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Challenge: Neural machine translation (NMT) is based on transformer models that are trained on general data from a single language pair or multiple languages.
Approach: They propose a method to make masks unique per domain to improve generalization to unseen domains.
Outcome: The proposed method outperforms continue training on multi-domain data on German to English translation by 1.47 BLEU points and on new domains by 1.52 BLUE points.

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