Jeewoo Sul

👋 Hello, I am Jeewoo Sul (설지우).

🙏 I’m currently rebuilding my homepage. Please wait until it’s done!

I am a research engineer at LG Electronics (Seoul, Korea). I obtained my M.S. in Computer Science at Hanyang University, where I was fortunate to be advised by Yong Suk Choi. Prior to that, I received B.S. in Computer Science from Kwangwoon University.

My research lies in the area of natural language proessing with large language models. I had mainly studied Abstractive Summarization and Prompt Tuning (2022-2023). I am currently excited about AI Coding Assistants, Code LLMs, and Code Generation.

news

Jun 18, 2024	I attend NAACL 2024 at Mexico City, Mexico and present our poster!
Feb 13, 2024	I’m starting a new position as a research engineer at LG Electronics!
Jul 15, 2023	I attend ACL 2023 at Toronto, Canada and do an oral presentation (Session: Summarization).

selected publications

NAACL short

Is Prompt Transfer Always Effective? An Empirical Study of Prompt Transfer for Question Answering

Minji Jung^*, Soyeon Park^*, Jeewoo Sul^*, and 1 more author

In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), Jun 2024

Abs DOI Code

Prompt tuning, which freezes all parameters of a pre-trained model and only trains a soft prompt, has emerged as a parameter-efficient approach. For the reason that the prompt initialization becomes sensitive when the model size is small, the prompt transfer that uses the trained prompt as an initialization for the target task has recently been introduced. Since previous works have compared tasks in large categories (e.g., summarization, sentiment analysis), the factors that influence prompt transfer have not been sufficiently explored. In this paper, we characterize the question answering task based on features such as answer format and empirically investigate the transferability of soft prompts for the first time. We analyze the impact of initialization during prompt transfer and find that the train dataset size of source and target tasks have the influence significantly. Furthermore, we propose a novel approach for measuring catastrophic forgetting and investigate how it occurs in terms of the amount of evidence. Our findings can help deeply understand transfer learning in prompt tuning.
ACL short

Balancing Lexical and Semantic Quality in Abstractive Summarization

Jeewoo Sul, and Yong Suk Choi

In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Jul 2023

Abs DOI Code

An important problem of the sequence-to-sequence neural models widely used in abstractive summarization is exposure bias. To alleviate this problem, re-ranking systems have been applied in recent years. Despite some performance improvements, this approach remains underexplored. Previous works have mostly specified the rank through the ROUGE score and aligned candidate summaries, but there can be quite a large gap between the lexical overlap metric and semantic similarity. In this paper, we propose a novel training method in which a re-ranker balances the lexical and semantic quality. We further newly define false positives in ranking and present a strategy to reduce their influence. Experiments on the CNN/DailyMail and XSum datasets show that our method can estimate the meaning of summaries without seriously degrading the lexical aspect. More specifically, it achieves an 89.67 BERTScore on the CNN/DailyMail dataset, reaching new state-of-the-art performance. Our code is publicly available at \urlhttps://github.com/jeewoo1025/BalSum.