Papers by Young Min Cho
Supplement Generation Training for Enhancing Agentic Task Performance (2026.findings-acl)
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Young Min Cho, Daniele Bonadiman, Divya Bhargavi, Tamer Alkhouli, Salvatore Romeo, Dongwei Jiang, Khushbu Pahwa, Yubin Ge, Etsuko Ishii, Monica Sunkara, Yi Zhang
| Challenge: | Training large foundation models for agentic tasks is impractical due to high computational costs, long iteration cycles, and rapid obsolescence as new models are released. |
| Approach: | They propose a method that trains a small LLM to generate supplemental text that helps the larger LLM solve the task more effectively. |
| Outcome: | The proposed approach decouples task-specific optimization from large foundation models . it achieves consistent and significant performance gains across diverse tasks and models - all without gradient access to the actor model. |
Language-based Valence and Arousal Expressions between the United States and China: a Cross-Cultural Examination (2025.findings-naacl)
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Young Min Cho, Dandan Pang, Stuti Thapa, Garrick Sherman, Lyle Ungar, Louis Tay, Sharath Chandra Guntuku
| Challenge: | valence and arousal are functionally equivalent across social media platforms . americans display higher emotional intensity than Chinese users . |
| Approach: | They compare valence and arousal on Twitter/X and Sina Weibo in China . they use the NRC-VAD lexicon to measure valance and valency . |
| Outcome: | The results show that the valence and arousal of the two platforms differ across cultures . the analysis also shows that the US users display higher emotional intensity than Chinese users . |
Unsupervised Entity Linking with Guided Summarization and Multiple-Choice Selection (2022.emnlp-main)
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| Challenge: | Entity linking is an important task for language understanding. |
| Approach: | They propose a fully unsupervised model that generates a guided summary of the contexts conditioning on a mention and then casts the task to a multiple-choice problem. |
| Outcome: | The proposed model achieves state-of-the-art performance on existing datasets and exiting datasets. |