Papers by Jungmin Choi
Identification of Multiple Logical Interpretations in Counter-Arguments (2025.emnlp-main)
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Wenzhi Wang, Paul Reisert, Shoichi Naito, Naoya Inoue, Machi Shimmei, Surawat Pothong, Jungmin Choi, Kentaro Inui
| Challenge: | Counter-arguments (CAs) are a good way to improve learners' critical thinking skills . however, it is difficult to provide every learner tailored feedback due to limited human resources and heavy workloads. |
| Approach: | They propose to annotate a dataset of 134 CAs annotated with 13 logical predicate questions and train a model with Reinforcement Learning with Verifiable Rewards to identify multiple logical interpretations. |
| Outcome: | The proposed model performs on par with larger proprietary models. |
RRADistill: Distilling LLMs’ Passage Ranking Ability for Long-Tail Queries Document Re-Ranking on a Search Engine (2024.emnlp-industry)
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Nayoung Choi, Youngjune Lee, Gyu-Hwung Cho, Haeyu Jeong, Jungmin Kong, Saehun Kim, Keunchan Park, Sarah Cho, Inchang Jeong, Gyohee Nam, Sunghoon Han, Wonil Yang, Jaeho Choi
| Challenge: | Large Language Models excel at understanding the semantic relationships between queries and documents, even with lengthy and complex long-tail queries. |
| Approach: | They propose an efficient label generation pipeline and novel sLLM training methods for both encoder and decoder models. |
| Outcome: | The proposed method improves re-ranking for long-tail queries on a Korean-based search platform. |
FinHarmBench: Financial Jailbreak Benchmark and Unsupervised Safety Fine-Tuning via Refusal Steering Distillation (2026.acl-industry)
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Yubin Choi, Yujin Yang, Subin Kim, Seokil Ham, Seungju Cho, Jungmin Son, Youngjun Kwak, Changick Kim
| Challenge: | Existing safety benchmarks focus on general harms and lack the granularity needed to capture domain-specific financial threats. |
| Approach: | They propose a benchmark to evaluate financially harmful and confusable benign prompts. |
| Outcome: | The proposed framework improves refusal behavior without annotating refusal responses. |
Flee the Flaw: Annotating the Underlying Logic of Fallacious Arguments Through Templates and Slot-filling (2024.emnlp-main)
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Irfan Robbani, Paul Reisert, Surawat Pothong, Naoya Inoue, Camélia Guerraoui, Wenzhi Wang, Shoichi Naito, Jungmin Choi, Kentaro Inui
| Challenge: | Prior work on quality assessment has focused on numerical scoring and fallacy type-labeling tasks, without aiming to analyze fallacy logic structures. |
| Approach: | They propose four sets of explainable templates for common informal logical fallacies designed to explicate a fallacy’s implicit logic. |
| Outcome: | The proposed models achieve a high agreement score and reasonable coverage 83% on 400 fallacious arguments and state-of-the-art language models struggle with detecting fallacy templates (0.47 accuracy). |
Progressive Multimodal Search and Reasoning for Knowledge-Intensive Visual Question Answering (2026.acl-long)
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| Challenge: | Existing approaches to knowledge-intensive visual question answering lack mechanisms to revise misdirected reasoning. |
| Approach: | They propose a framework that progressively constructs a structured reasoning trajectory . they use dual-scope queries to retrieve diverse knowledge from heterogeneous knowledge bases . |
| Outcome: | The proposed framework improves retrieval recall and end-to-end answer accuracy. |
Designing Logic Pattern Templates for Counter-Argument Logical Structure Analysis (2024.findings-emnlp)
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Shoichi Naito, Wenzhi Wang, Paul Reisert, Naoya Inoue, Camélia Guerraoui, Kenshi Yamaguchi, Jungmin Choi, Irfan Robbani, Surawat Pothong, Kentaro Inui
| Challenge: | Despite their effectiveness, the logical attack structure of counterarguments remains unexplored due to its complexity. |
| Approach: | They propose a task to analyze logical attack structure of counterarguments in relation to their corresponding opponent argument using 10 new CA logic patterns. |
| Outcome: | The proposed task achieves high annotator agreement and coverage and high coverage on a dataset of 778 CAs. |