116 citations · 146 across the 10 of their papers we have counts for
10 papers
PertReason: A Knowledge-Grounded Benchmark and Framework for Cell-State-Conditioned Mechanistic Reasoning of Perturbation Effects
Dongkwan Kim, Yiming Gao, Yining Yang +1
Evaluating machine learning in scientific domains requires separating correct predictions from correct reasons under realistic distribution shifts. We introduce PertReason, a knowl…
MUG-Eval: A Proxy Evaluation Framework for Multilingual Generation Capabilities in Any Language
Seyoung Song, Seogyeong Jeong, Eunsu Kim +4
Evaluating text generation capabilities of large language models (LLMs) is challenging, particularly for low-resource languages where methods for direct assessment are scarce. We p…
Measuring Interest Group Positions on Legislation: An AI-Driven Analysis of Lobbying Reports
Jiseon Kim, Dongkwan Kim, Joohye Jeong +2
Special interest groups (SIGs) in the U.S. participate in a range of political activities, such as lobbying and making campaign donations, to influence policy decisions in the legi…
LLM-as-an-Interviewer: Beyond Static Testing Through Dynamic LLM Evaluation
Eunsu Kim, Juyoung Suk, Seungone Kim +3
We introduce LLM-as-an-Interviewer, a novel paradigm for evaluating large language models (LLMs). This approach leverages multi-turn interactions where the LLM interviewer actively…
Generalizing Weisfeiler-Lehman Kernels to Subgraphs
Dongkwan Kim, Alice Oh
Subgraph representation learning has been effective in solving various real-world problems. However, current graph neural networks (GNNs) produce suboptimal results for subgraph-le…
Perceptions to Beliefs: Exploring Precursory Inferences for Theory of Mind in Large Language Models
Chani Jung, Dongkwan Kim, Jiho Jin +5
While humans naturally develop theory of mind (ToM), the capability to understand other people's mental states and beliefs, state-of-the-art large language models (LLMs) underperfo…
Models and Benchmarks for Representation Learning of Partially Observed Subgraphs
Dongkwan Kim, Jiho Jin, Jaimeen Ahn +1
Subgraphs are rich substructures in graphs, and their nodes and edges can be partially observed in real-world tasks. Under partial observation, existing node- or subgraph-level mes…
How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision
Dongkwan Kim, Alice Oh
Attention mechanism in graph neural networks is designed to assign larger weights to important neighbor nodes for better representation. However, what graph attention learns is not…
Translating Subgraphs to Nodes Makes Simple GNNs Strong and Efficient for Subgraph Representation Learning
Dongkwan Kim, Alice Oh
Subgraph representation learning has emerged as an important problem, but it is by default approached with specialized graph neural networks on a large global graph. These models d…
Homogeneity-Based Transmissive Process to Model True and False News in Social Networks
Jooyeon Kim, Dongkwan Kim, Alice Oh
An overwhelming number of true and false news stories are posted and shared in social networks, and users diffuse the stories based on multiple factors. Diffusion of news stories f…