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Documents authored by Hwang, Seung-won

Artifact
Software
DOI: 10.4230/artifacts.23374
Chain-of-Grounded-Objectives

Authors: Sangyeop Yeo, Seung-Won Hwang, and Yu-Seung Ma

Abstract
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Sangyeop Yeo, Seung-Won Hwang, Yu-Seung Ma. Chain-of-Grounded-Objectives (Software, Source Code). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@misc{dagstuhl-artifact-23374,
   title = {{Chain-of-Grounded-Objectives}}, 
   author = {Yeo, Sangyeop and Hwang, Seung-Won and Ma, Yu-Seung},
   note = {Software, version 1.0., swhId: \href{https://archive.softwareheritage.org/swh:1:dir:315704008a9f9a515047943266a959518485cbb0}{\texttt{swh:1:dir:315704008a9f9a515047943266a959518485cbb0}} (visited on 2025-06-25)},
   url = {https://github.com/DDIDUs/Chain-of-Grounded-Objectives},
   doi = {10.4230/artifacts.23374},
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2025.35
Chain of Grounded Objectives: Concise Goal-Oriented Prompting for Code Generation

Authors: Sangyeop Yeo, Seung-Won Hwang, and Yu-Seung Ma

Published in: LIPIcs, Volume 333, 39th European Conference on Object-Oriented Programming (ECOOP 2025)

Abstract
The use of Large Language Models (LLMs) for code generation has gained significant attention in recent years. Existing methods often aim to improve the quality of generated code by incorporating additional contextual information or guidance into input prompts. Many of these approaches adopt process-oriented reasoning strategies, mimicking human-like step-by-step thinking; however, they may not always align with the structured nature of programming languages. This paper introduces Chain of Grounded Objectives (CGO), a concise goal-oriented prompting approach that embeds functional objectives into prompts to enhance code generation. By focusing on precisely defined objectives rather than explicit procedural steps, CGO aligns more naturally with programming tasks while retaining flexibility. Empirical evaluations on HumanEval, MBPP, their extended versions, and LiveCodeBench show that CGO achieves accuracy comparable to or better than existing methods while using fewer tokens, making it a more efficient approach to LLM-based code generation.
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Sangyeop Yeo, Seung-Won Hwang, and Yu-Seung Ma. Chain of Grounded Objectives: Concise Goal-Oriented Prompting for Code Generation. In 39th European Conference on Object-Oriented Programming (ECOOP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 333, pp. 35:1-35:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{yeo_et_al:LIPIcs.ECOOP.2025.35,
  author =	{Yeo, Sangyeop and Hwang, Seung-Won and Ma, Yu-Seung},
  title =	{{Chain of Grounded Objectives: Concise Goal-Oriented Prompting for Code Generation}},
  booktitle =	{39th European Conference on Object-Oriented Programming (ECOOP 2025)},
  pages =	{35:1--35:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-373-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{333},
  editor =	{Aldrich, Jonathan and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2025.35},
  URN =		{urn:nbn:de:0030-drops-233271},
  doi =		{10.4230/LIPIcs.ECOOP.2025.35},
  annote =	{Keywords: Artificial Intelligence, Natural Language Processing, Prompt Design, Large Language Models, Code Generation}
}
Document
DOI: 10.4230/DagSemProc.08421.4
08421 Working Group: Explanation

Authors: Hidir Aras, Norbert Fuhr, Seung-won Hwang, Ander de Keijzer, Friederike Klan, Hans-Joachim Lenz, Tom Matthé, Heinz Schweppe, Mirco Stern, and Guy De Tré

Published in: Dagstuhl Seminar Proceedings, Volume 8421, Uncertainty Management in Information Systems (2009)

Abstract
This working group addressed the issue of explaining the results of an uncertainty information system to a user. For that, we structured the problem along three major queries: why, what, and how.
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Hidir Aras, Norbert Fuhr, Seung-won Hwang, Ander de Keijzer, Friederike Klan, Hans-Joachim Lenz, Tom Matthé, Heinz Schweppe, Mirco Stern, and Guy De Tré. 08421 Working Group: Explanation. In Uncertainty Management in Information Systems. Dagstuhl Seminar Proceedings, Volume 8421, pp. 1-3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{aras_et_al:DagSemProc.08421.4,
  author =	{Aras, Hidir and Fuhr, Norbert and Hwang, Seung-won and de Keijzer, Ander and Klan, Friederike and Lenz, Hans-Joachim and Matth\'{e}, Tom and Schweppe, Heinz and Stern, Mirco and De Tr\'{e}, Guy},
  title =	{{08421 Working Group: Explanation}},
  booktitle =	{Uncertainty Management in Information Systems},
  pages =	{1--3},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8421},
  editor =	{Christoph Koch and Birgitta K\"{o}nig-Ries and Volker Markl and Maurice van Keulen},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08421.4},
  URN =		{urn:nbn:de:0030-drops-19359},
  doi =		{10.4230/DagSemProc.08421.4},
  annote =	{Keywords: Probabilistic databases, explanation component; transparenca; sources of uncertainty; presenting uncertainty}
}
Document
DOI: 10.4230/DagSemProc.08421.9
A Uncertainty Perspective on Qualitative Preference

Authors: Seung-won Hwang and Mu-Woong Lee

Published in: Dagstuhl Seminar Proceedings, Volume 8421, Uncertainty Management in Information Systems (2009)

Abstract
Collaborative filtering has been successfully applied for predicting a person's preference on an item, by aggregating community preference on the item. Typically, collaborative filtering systems are based on based on quantitative preference modeling, which requires users to express their preferences in absolute numerical ratings. However, quantitative user ratings are known to be biased and inconsistent and also significantly more burdensome to the user than the alternative qualitative preference modeling, requiring only to specify relative preferences between the item pair. More specifically, we identify three main components of collaborative filtering-- preference representation, aggregation, and similarity computation, and view each component from a qualitative perspective. From this perspective, we build a framework, which collects only qualitative feedbacks from users. Our rating-oblivious framework was empirically validated to have comparable prediction accuracies to an (impractical) upper bound accuracy obtained by collaborative filtering system using ratings.
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Seung-won Hwang and Mu-Woong Lee. A Uncertainty Perspective on Qualitative Preference. In Uncertainty Management in Information Systems. Dagstuhl Seminar Proceedings, Volume 8421, pp. 1-9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{hwang_et_al:DagSemProc.08421.9,
  author =	{Hwang, Seung-won and Lee, Mu-Woong},
  title =	{{A Uncertainty Perspective on Qualitative Preference}},
  booktitle =	{Uncertainty Management in Information Systems},
  pages =	{1--9},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8421},
  editor =	{Christoph Koch and Birgitta K\"{o}nig-Ries and Volker Markl and Maurice van Keulen},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08421.9},
  URN =		{urn:nbn:de:0030-drops-19323},
  doi =		{10.4230/DagSemProc.08421.9},
  annote =	{Keywords: Collaborative filtering, qualitative preference, uncertainty}
}
Document
DOI: 10.4230/DagSemProc.08421.11
Recommendation: A Less Explored Killer-App of Uncertainty?

Authors: Seung-won Hwang and Jong-won Roh

Published in: Dagstuhl Seminar Proceedings, Volume 8421, Uncertainty Management in Information Systems (2009)

Abstract
Due to the unprecedented amount of information available, it is becoming more and more important to provide personalized recommendations on data, based on past user feedbacks. However, available user feedbacks or ratings are extremely sparse, which motivates the needs for rating prediction. The most widely adopted solution has been collaborative filtering, which (1) identifies "neighboring" users with similar tastes and (2) aggregates their ratings to predict the ratings of the given user. However, while each of such aggregation involves varying levels of uncertainty, e.g., depending on the distribution of ratings aggregated, which has not been systematically considered in recommendation, though recent study suggests such consideration can boost prediction accuracy. To consider uncertainty in rating prediction, this paper reformulates the collaborative filtering problem as aggregating community ratings into multiple predicted ratings with varying levels of certainty, based on which we identify top-k results with both high confidence and rating. We empirically study the efficiency and accuracy of our proposed framework, over a classical collaborative filtering system.
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Seung-won Hwang and Jong-won Roh. Recommendation: A Less Explored Killer-App of Uncertainty?. In Uncertainty Management in Information Systems. Dagstuhl Seminar Proceedings, Volume 8421, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{hwang_et_al:DagSemProc.08421.11,
  author =	{Hwang, Seung-won and Roh, Jong-won},
  title =	{{Recommendation: A Less Explored Killer-App of Uncertainty?}},
  booktitle =	{Uncertainty Management in Information Systems},
  pages =	{1--6},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8421},
  editor =	{Christoph Koch and Birgitta K\"{o}nig-Ries and Volker Markl and Maurice van Keulen},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08421.11},
  URN =		{urn:nbn:de:0030-drops-19336},
  doi =		{10.4230/DagSemProc.08421.11},
  annote =	{Keywords: Recommendation, uncertainty}
}
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