Papers by Hyundong Justin Cho
Tuning-Free Personalized Alignment via Trial-Error-Explain In-Context Learning (2025.findings-naacl)
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Hyundong Justin Cho, Karishma Sharma, Nicolaas Paul Jedema, Leonardo F. R. Ribeiro, Jonathan May, Alessandro Moschitti
| Challenge: | Language models are biased towards generic outputs as they are trained to align to an aggregate preference to be generally useful. |
| Approach: | They propose a tuning-free method that personalizes language models for text generation tasks with fewer than 10 examples per user. |
| Outcome: | The proposed method achieves favorable win rates on pairwise comparisons with the previous state-of-the-art and outperforms competitive tuning-free baselines for personalized alignment tasks of writing emails, essays and news articles. |
Can Vision Language Models Understand Mimed Actions? (2025.findings-acl)
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Hyundong Justin Cho, Spencer Lin, Tejas Srinivasan, Michael Saxon, Deuksin Kwon, Natali T. Chavez, Jonathan May
| Challenge: | Nonverbal communication (NVC) is an integral part of human language, but it has been overlooked in natural language processing research. |
| Approach: | They propose a multimodal multimodal recognition task that uses a corpus of mimed gestures to evaluate their understanding of NVC. |
| Outcome: | The proposed task is based on 86 unique gestures with perturbations applied to avatar, background, and viewpoint for evaluating recognition robustness. |
NewsInterview: a Dataset and a Playground to Evaluate LLMs’ Grounding Gap via Informational Interviews (2025.acl-long)
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Alexander Spangher, Michael Lu, Sriya Kalyan, Hyundong Justin Cho, Tenghao Huang, Weiyan Shi, Jonathan May
| Challenge: | Existing large datasets (1k-10k transcripts) are generated via crowdsourcing and are inherently unnatural. |
| Approach: | They curate a dataset of 40,000 two-person informational interviews from NPR and CNN . they find that LLMs are significantly less likely than human interviewers to use acknowledgements and pivot to higher-level questions. |
| Outcome: | The proposed model is based on 40,000 interviews with journalists and CNN . |
Uncovering Intervention Opportunities for Suicide Prevention with Language Model Assistants (2026.acl-long)
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Jaspreet Ranjit, Hyundong Justin Cho, Claire J. Smerdon, Yoonsoo Nam, Myles Phung, Jonathan May, John R. Blosnich, Swabha Swayamdipta
| Challenge: | Using language models, annotators can help develop novel suicide interventions . 85% of cases where LM predictions disagree with existing annotations are analyzed . |
| Approach: | They propose a human-in-the-loop algorithm that leverages language models as an assistant to annotators and experts to facilitate data-driven insights from NVDRS data. |
| Outcome: | The proposed algorithm can be used to support the development of novel suicide interventions . it finds that LM predictions match existing data annotations about 85% of the time . |