Papers by Youngbin Choi
Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentation (2024.lrec-main)
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| Challenge: | Existing methods to augment textual data are limited due to the discrete characteristics of the textual dataset. |
| Approach: | They propose a decision-boundary-aware data augmentation strategy to enhance robustness using pretrained language models by shifting latent features closer to the decision boundary and reconstruction to generate an ambiguous version with a soft label. |
| Outcome: | The proposed method performs better than existing methods and is extensible with curriculum data augmentation. |
Plane Geometry Problem Solving with Multi-modal Reasoning: A Survey (2026.findings-eacl)
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| Challenge: | Plane geometry problem solving has gained significant attention as a benchmark to assess the multi-modal reasoning capabilities of large vision-language models. |
| Approach: | They present a systematic review of existing work in PGPS and summarize their results. |
| Outcome: | The proposed frameworks are compared with existing frameworks and analyze them according to their architectural designs. |
ChronoBias: A Benchmark for Evaluating Temporal Group Bias in the Time-sensitive Knowledge of Large Language Models (2025.findings-emnlp)
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| Challenge: | Using a template-based semi-automated generation method, we evaluate time-conditional group bias in time-sensitive knowledge of large language models (LLMs). |
| Approach: | They propose a template-based semi-automated generation method to construct a time-conditional group bias benchmark. |
| Outcome: | The proposed method balancing quality-quantity trade-off in existing benchmark curation approaches. |
Crowdsourcing in the Development of a Multilingual FrameNet: A Case Study of Korean FrameNet (2020.lrec-1)
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| Challenge: | Using current methods, the construction of multilingual FrameNets is expensive and complex. |
| Approach: | They evaluated whether crowdsourcing approaches captured cross-cultural and cross-linguistic meanings . they found that crowd workers made intuitive choices comparable to trained FrameNet experts . |
| Outcome: | The results are now available in Korean FrameNet 1.1. |
GeoDANO: Geometric VLM with Domain Agnostic Vision Encoder (2025.findings-emnlp)
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| Challenge: | GeoDANO is a geometric vision-language model with a domain-agnostic vision encoder . it is currently limited to recognizing geometric features in general-purpose VLMs . |
| Approach: | They propose a geometric vision-language model with a domain-agnostic vision encoder for plane geometry problems. |
| Outcome: | The proposed model outperforms vision encoders in recognizing geometric features . it outperformed specialized methods for plane geometry problems and GPT-4o on MathVerse . |
CoPL: Collaborative Preference Learning for Personalizing LLMs (2025.emnlp-main)
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| Challenge: | Existing methods for personalizing large language models struggle with flexibility and generalization. |
| Approach: | They propose a graph-based collaborative filtering framework that models user-response relationships to enhance preference estimation in sparse annotation settings. |
| Outcome: | The proposed framework outperforms existing reward models in TL;DR, UltraFeedback-P, and PersonalLLM datasets. |