Papers by Sungjun Lim
Modularized Multilingual NMT with Fine-grained Interlingua (2024.naacl-long)
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| Challenge: | Neural Machine Translation (MNMT) systems lack layer-sharing to generate interlingua features . however, layer-share structure does not guarantee explicit propagation of language-specific features to respective decoders. |
| Approach: | They propose to share top of language-specific encoder layers to enable interlingua features . their method demonstrates an improved average BLEU score by "+2.90" in En-to-Any directions . |
| Outcome: | The proposed approach improves the BLEU score by "+2.90" in En-to-Any directions and by "+1.06" in zero-shot translation. |
Uncertainty-Aware Contrastive Decoding (2025.findings-acl)
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| Challenge: | Large language models excel in a wide range of tasks, but generating factually accurate outputs remains a challenge. |
| Approach: | They propose a method that dynamically adjusts model contributions at each decoding step based on uncertainty. |
| Outcome: | The proposed method significantly improves factual accuracy and reliability over existing methods. |