One-sentence Summary: We propose a method that self-supervise graph attention through edges and it should be designed according to the average degree and homophily of graphs.

Paper arXiv Code Slide

Efficient Representation Learning of Subgraphs by Subgraph-To-Node Translation

Dongkwan Kim and Alice Oh. Workshop on Geometrical and Topological Representation Learning at ICLR (ICLR GTRL), 2022

One-sentence Summary: We propose Subgraph-To-Node translation to efficiently learn representations of subgraphs by coarsely translating subgraphs into nodes.

Paper arXiv

Models and Benchmarks for Representation Learning of Partially Observed Subgraphs

Dongkwan Kim, Jiho Jin, Jaimeen Ahn and Alice Oh. International Conference on Information and Knowledge Management (CIKM), 2022

Paper arXiv Code

Translating Subgraphs to Nodes Makes Simple GNNs Strong and Efficient for Subgraph Representation Learning

Dongkwan Kim and Alice Oh. International Conference on Machine Learning (ICML), 2024

One-sentence Summary: We propose Subgraph-To-Node translation to effectively and efficiently learn representations of subgraphs by coarsely translating subgraphs into nodes.

Paper arXiv Code Slide

Perceptions to Beliefs: Exploring Precursory Inferences for Theory of Mind in Large Language Models

Chani Jung, Dongkwan Kim, Jiho Jin, Jiseon Kim, Yeon Seonwoo, Yejin Choi, Alice Oh, and Hyunwoo Kim. Empirical Methods in Natural Language Processing (EMNLP), 2024

One-sentence Summary: We assess key precursors of Theory of Mind (ToM) in LLMs by perception-augmented ToM benchmarks. We propose PercepToM, a ToM method inspired by our findings of models’ strength in perception inference and weakness in perception-to-belief inference.

Paper arXiv

Understanding Lobbying Strategies in Legislative Process: Bill Position Dataset and Lobbying Analysis

Jiseon Kim, Dongkwan Kim, Joohye Jeong, In Song Kim, Alice Oh. Workshop for Women in Machine Learning at NeurIPS (NeurIPS WiML), 2024

Slide

Salad-Bowl-LLM: Multi-Culture LLMs by In-Context Demonstrations from Diverse Cultures

Dongkwan Kim, Junho Myung, and Alice Oh. Workshop on Socially Responsible Language Modelling Research at NeurIPS (NeurIPS SoLaR), 2024

One-sentence Summary: We explore the use of in-context learning with diverse demonstrations to enhance Large Language Models’ cultural understanding.

Paper

Measuring Interest Group Positions on Legislation: An AI-Driven Analysis of Lobbying Reports

Jiseon Kim, Dongkwan Kim, Joohye Jeong, Alice Oh, In Song Kim. Arxiv, 2025

arXiv

Generalizing Weisfeiler-Lehman Kernels to Subgraphs

Dongkwan Kim and Alice Oh. International Conference on Learning Representations (ICLR), 2025

One-sentence Summary: We propose WLKS that extends the WL graph kernel to subgraphs, captures high-order structural similarities in k-hop neighborhoods, efficiently outperforming state-of-the-art GNNs.

Paper arXiv Code Slide

LLM-as-an-Interviewer: Beyond Static Testing Through Dynamic LLM Evaluation

Eunsu Kim, Juyoung Suk, Seungone Kim, Niklas Muennighoff, Dongkwan Kim, Alice Oh. Findings of the Association for Computational Linguistics: ACL 2025 (ACL-Findings 2025, Long), 2025

One-sentence Summary: We propose a new paradigm for evaluating large language models (LLMs), called LLM-as-an-Interviewer.

Paper arXiv Code

MUG-Eval: A Proxy Evaluation Framework for Multilingual Generation Capabilities in Any Language

Seyoung Song, Seogyeong Jeong, Eunsu Kim, Jiho Jin, Dongkwan Kim, Jay Shin, Alice Oh. Findings of the Empirical Methods in Natural Language Processing: EMNLP 2025 (EMNLP-Findings 2025, Long), 2025

One-sentence Summary: A language-agnostic framework that evaluates LLM multilingual generation by measuring task completion rates in self-communication scenarios, enabling objective assessment across 2,100+ languages without requiring language-specific tools or humans.

arXiv Code

Dongkwan Kim

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