Papers by Eunjung Cho
[b] = [d] - [t] + [p]: Self-supervised Speech Models Discover Phonological Vector Arithmetic (2026.findings-acl)
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| Challenge: | Existing studies on how self-supervised speech models encode rich phonetic information have not explored how they are structured. |
| Approach: | They conduct a comprehensive analysis of the underlying structure of S3M representations with particular attention to phonological vectors. |
| Outcome: | The proposed model encodes phonologically interpretable and compositional vectors, demonstrating phonology vector arithmetic. |
Aligning Large Language Models with Diverse Political Viewpoints (2024.emnlp-main)
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| Challenge: | Large language models such as ChatGPT exhibit striking political biases . a recent study shows that chatbots exhibit progressive, liberal, and proenvironmental biase . |
| Approach: | They propose to align large language models with 100,000 comments from candidates running for national parliament in Switzerland. |
| Outcome: | The proposed model generates more accurate political viewpoints from Swiss parties compared to commercial models such as ChatGPT. |
Hermit Kingdom Through the Lens of Multiple Perspectives: A Case Study of LLM Hallucination on North Korea (2025.coling-main)
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| Challenge: | Existing solutions to hallucination in large language models (LLMs) focus on aligning models with credible sources or improving how models communicate their confidence in outputs. |
| Approach: | They examine how best-performing multilingual LLMs and specific language-based models generate information about North Korea in three languages spoken in countries with significant geo-political interests. |
| Outcome: | The best-performing models generate information in three languages spoken in countries with significant geo-political interests: English (United States, United Kingdom), Korean (South Korea), and Mandarin Chinese (China). |
NLP for Social Good: A Survey and Outlook of Challenges, Opportunities and Responsible Deployment (2026.eacl-long)
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Antonia Karamolegkou, Angana Borah, Eunjung Cho, Sagnik Ray Choudhury, Martina Galletti, Pranav Gupta, Oana Ignat, Priyanka Kargupta, Neema Kotonya, Hemank Lamba, Sun-Joo Lee, Arushi Mangla, Ishani Mondal, Fatima Zahra Moudakir, Deniz Nazar, Poli Nemkova, Dina Pisarevskaya, Naquee Rizwan, Nazanin Sabri, Keenan Samway, Dominik Stammbach, Anna Steinberg Schulten, David Tomás, Steven R Wilson, Bowen Yi, Jessica H Zhu, Arkaitz Zubiaga, Anders Søgaard, Alexander Fraser, Zhijing Jin, Rada Mihalcea, Joel R. Tetreault, Daryna Dementieva
| Challenge: | This paper surveys work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
| Approach: | This paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
| Outcome: | The paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |