Papers by Sungduk Yu
Probing Semantic Routing in Large Mixture-of-Expert Models (2025.findings-emnlp)
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| Challenge: | large mixture-of-expert models have become increasingly common in the open domain . prior work has explored functional differentiation through routing behavior . |
| Approach: | They investigate whether expert routing in large mixture-of-expert models is influenced by the semantics of the inputs. |
| Outcome: | The results show that expert routing is influenced by the semantics of the inputs. |
Why do LLaVA Vision-Language Models Reply to Images in English? (2024.findings-emnlp)
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Musashi Hinck, Carolin Holtermann, Matthew Olson, Florian Schneider, Sungduk Yu, Anahita Bhiwandiwalla, Anne Lauscher, Shao-Yen Tseng, Vasudev Lal
| Challenge: | Including an image in a multimodal query significantly increases the likelihood of the model returning an English response regardless of the language of the query. |
| Approach: | They propose a two-pronged approach that combines extensive ablation of the design space with a mechanistic analysis of the models’ internal representations of image and text inputs. |
| Outcome: | The proposed approach reduces the multilingual error by switching the language backbone for a bilingual language model. |
LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model Compression (2025.findings-naacl)
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Souvik Kundu, Anahita Bhiwandiwalla, Sungduk Yu, Phillip Howard, Tiep Le, Sharath Nittur Sridhar, David Cobbley, Hao Kang, Vasudev Lal
| Challenge: | LVLMs have been shown to perform well on simple uni-modal benchmarks, but their detailed study on multi-modal models is still lacking. |
| Approach: | They propose a framework to analyze the impact of compression on LVLMs on multi-modal input driven tasks. |
| Outcome: | The proposed framework analyzes the impact of compression on generative performance of large vision language models on multi-modal input driven tasks. |