Papers by Jinyoung Park
Can Code-Switched Texts Activate a Knowledge Switch in LLMs? A Case Study on English-Korean Code-Switching (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Recent large language models (LLMs) demonstrate multilingual abilities, yet they are English-centric due to dominance of English in training corpora. |
| Approach: | They propose to use a synthetic English-korean CS question-answering dataset to investigate this potential. |
| Outcome: | The proposed model can activate, identify and leverage knowledge for reasoning in low-resource languages. |
LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical Study (2025.acl-long)
Copied to clipboard
Dongil Yang, Minjin Kim, Sunghwan Kim, Beong-woo Kwak, Minjun Park, Jinseok Hong, Woontack Woo, Jinyoung Yeo
| Challenge: | Large language models (LLMs) have demonstrated impressive progress in various text-based tasks, such as question-answering and content generation. |
| Approach: | They propose a benchmark to evaluate Large Language Models’ ability to understand scene graphs and generate them from textual narratives. |
| Outcome: | The proposed model performs well on scene graph understanding but struggles with scene graph generation, particularly for complex narratives. |
One Missing Piece for Open-Source Reasoning Models: A Dataset to Mitigate Cold-Starting Short CoT LLMs in RL (2025.acl-industry)
Copied to clipboard
Hyungjoo Chae, Dongjin Kang, Jihyuk Kim, Beong-woo Kwak, Sunghyun Park, Haeju Park, Jinyoung Yeo, Moontae Lee, Kyungjae Lee
| Challenge: | Existing large reasoning models are limited by their closed nature and high API costs and safety issues. |
| Approach: | They propose to build a long CoT dataset with existing short CoT LLMs that are not trained for inference-time scaling. |
| Outcome: | The proposed model achieves quality comparable to—or slightly below—R1 and is able to think longer and provide control over the thought budget to better manage the overthinking problem. |
Soft Representation Learning for Sparse Transfer (P19-1)
Copied to clipboard
| Challenge: | Using adversarial training, we can “soft-code” shared and private spaces to avoid sparse sharing. |
| Approach: | They propose to use adversarial training to “soft-code” shared and private spaces to avoid the shared space gets too sparse. |
| Outcome: | The proposed architecture avoids sparse sharing of shared and private spaces, and also deals with low-quality input. |
Generative Subgraph Retrieval for Knowledge Graph–Grounded Dialog Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for knowledge graph–grounded dialog generation fail to leverage the rich knowledge of pretrained language models. |
| Approach: | They propose a method for dialog generation that integrates dialog history with a knowledge graph. |
| Outcome: | The proposed method achieves state-of-the-art in knowledge graph–grounded dialog generation on OpenDialKG and KOMODIS datasets. |
Less is More: Attention Supervision with Counterfactuals for Text Classification (2020.emnlp-main)
Copied to clipboard
| Challenge: | Specifically, we explore the advantage of counterfactual reasoning, over associative reasoning . Adding human supervision to attention has been shown to improve model predictions and explanations . |
| Approach: | They propose to use machine-augmented human attention supervision to enhance model quality. |
| Outcome: | The proposed method is more effective than existing methods requiring higher annotation cost . the proposed method can be trained to generate similar attention to human supervision . |
Detecting Bipolar Disorder from Misdiagnosed Major Depressive Disorder with Mood-Aware Multi-Task Learning (2024.naacl-long)
Copied to clipboard
| Challenge: | Bipolar Disorder (BD) is a mental disorder characterized by intense mood swings, ranging from depression to manic states. |
| Approach: | They propose to use social media data to identify BD risk in individuals misdiagnosed as MDD by multi-task learning. |
| Outcome: | The proposed approach outperforms state-of-the-art baselines and can provide insights into the impact of BD mood on future risk. |
Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing suicide dictionaries for other languages have been limited to Korean . a model that uses social media data to identify whether a post includes suicidal ideation is useful . |
| Approach: | They propose a model that uses existing suicide dictionaries for Korean to predict suicidal ideation . they use the existing dictionary for English and Chinese to translate a post into English and then use the separate suicide-oriented embeddings for English. |
| Outcome: | The proposed model can detect whether a given social media post includes suicidal ideation in Korean . it uses existing suicide dictionaries for other languages to translate the post into English and Chinese, and then embeds the suicide-oriented embeddings for English and China. |
Learning with Limited Data for Multilingual Reading Comprehension (D19-1)
Copied to clipboard
| Challenge: | Existing approaches to support question answering in a new language with limited training resources introduce noises to the training data due to translation or generation errors. |
| Approach: | They propose a weakly-supervised framework that quantifies noises from automatically generated labels to deemphasize or fix noisy data in training. |
| Outcome: | The proposed framework can deemphasize or fix noisy data in training on low-resource languages with varying similarity to English. |
PAC-BENCH: Evaluating Multi-Agent Collaboration under Privacy Constraints (2026.findings-acl)
Copied to clipboard
Minjun Park, Donghyun Kim, Hyeonjong Ju, Seungwon Lim, Dongwook Choi, Taeyoon Kwon, Minju Kim, Jinyoung Yeo
| Challenge: | Recent research explores multi-agent systems where agents collaborate toward shared goals to handle complex tasks. |
| Approach: | They propose a benchmark for systematic evaluation of multi-agent collaboration under privacy constraints. |
| Outcome: | The proposed benchmark shows that privacy constraints degrade collaboration performance and make outcomes depend more on the initiating agent than the partner. |