Papers by Sangmin Lee
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. |
A Few Bad Apples Spoil the Bunch: Preventing Global Entropy Collapse Driven by a Small Set of Tokens in LLM Reasoning (2026.findings-acl)
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| Challenge: | Reinforcement learning with verifiable rewards and Reinforced Learning from internal feedback fail to benefit from test-time compute due to entropy collapse and the resulting loss of reasoning diversity. |
| Approach: | They propose a strategy that assigns each generated token a redistribution score and applies selective KL regularization to only the top 5% of tokens under this score. |
| Outcome: | The proposed model improves on both RLVR and RLIF models on math reasoning benchmarks, showing that targeted entropy control at a vanishingly small subset of tokens is sufficient to sustain reasoning diversity and effective test-time scaling. |
HiKE: Hierarchical Evaluation Framework for Korean-English Code-Switching Speech Recognition (2026.findings-eacl)
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| Challenge: | Recent advances in automatic speech recognition (ASR) have pushed error rates below 5% on standard monolingual benchmarks. |
| Approach: | They propose a framework for the evaluation of multilingual ASR models using loanword labels and a hierarchical CS-level labeling scheme that allows for fine-tuning with synthetic CS data. |
| Outcome: | The proposed framework provides a means for the precise evaluation of multilingual ASR models and fosters research in the field. |
UniCoM: A Universal Code-Switching Speech Generator (2025.findings-emnlp)
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| Challenge: | Code-switching (CS) is a common phenomenon in real-world conversations and poses significant challenges for multilingual speech technology. |
| Approach: | They propose a pipeline for generating high-quality, natural CS samples without altering sentence semantics. |
| Outcome: | The proposed pipeline generates high-quality, natural CS samples without altering sentence semantics without alteration of sentence semantic. |
AutoAugment Is What You Need: Enhancing Rule-based Augmentation Methods in Low-resource Regimes (2024.eacl-srw)
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| Challenge: | Existing methods for text data augmentation suffer from potential semantic damage due to the discrete nature of sentences. |
| Approach: | They propose to adapt AutoAugment to solve this problem by using softEDA to increase text data. |
| Outcome: | The proposed method can boost existing augmentation methods and enhance cutting-edge pretrained language models. |