Papers by Yohan Na
SentiCSE: A Sentiment-aware Contrastive Sentence Embedding Framework with Sentiment-guided Textual Similarity (2024.lrec-main)
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| Challenge: | Sentiment-aware pre-trained language models (PLMs) demonstrate impressive results in downstream sentiment analysis tasks, but they neglect to evaluate the quality of constructed sentiment representations. |
| Approach: | They propose a new metric for evaluating the quality of sentiment representations that is based on the degree of equivalence in sentiment polarity between two sentences. |
| Outcome: | The proposed framework outperforms the existing sentiment-aware models in sentiment analysis tasks. |
ixi-GEN: Efficient Industrial sLLMs through Domain Adaptive Continual Pretraining (2025.emnlp-industry)
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Seonwu Kim, Yohan Na, Kihun Kim, Hanhee Cho, Geun Lim, Mintae Kim, Seongik Park, Ki Hyun Kim, Youngsub Han, Byoung-Ki Jeon
| Challenge: | Domain Adaptive Continual Pretraining (DACP) is a method to mitigate performance degradation in small LLMs and enhance their effectiveness in target domains. |
| Approach: | They propose a continual pretraining methodology that optimizes sLLMs within service domains and enhances their effectiveness in target domains. |
| Outcome: | The proposed model achieves significant gains in target-domain performance while preserving general capabilities, offering a cost-efficient and scalable solution for enterprise-level deployment. |