Papers by Minwoo Lee
Target-Agnostic Gender-Aware Contrastive Learning for Mitigating Bias in Multilingual Machine Translation (2023.emnlp-main)
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| Challenge: | Gender bias is a significant issue in machine translation, but most studies focus on debiasing bilingual models without consideration for multilingual systems. |
| Approach: | They propose a method which debiases bilingual models for unambiguous cases where there is a single correct translation. |
| Outcome: | The proposed method improves gender accuracy by a wide margin without hampering translation performance. |
From Local to Global: Revisiting Structured Pruning Paradigms for Large Language Models (2026.acl-long)
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Ziyan Wang, Enmao Diao, Qi Le, Pu Wang, Minwoo Lee, Shu-ping Yeh, Evgeny Stupachenko, Hao Feng, Li Yang
| Challenge: | Structured pruning is a practical approach to deploying large language models (LLMs) but it fails to capitalize on modest task-specific calibration signals, causing limited downstream gains. |
| Approach: | They propose a method that removes attention heads and MLP channels using loss-based important scores . they use perplexity for language modeling and a margin-based objective for decision-style tasks . |
| Outcome: | The proposed method lowers perplexity and improves accuracy at higher sparsity . it also stabilizes accuracy and mitigates perxity collapse without fine-tuning . |
Can LLMs Recognize Toxicity? A Structured Investigation Framework and Toxicity Metric (2024.findings-emnlp)
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| Challenge: | Existing toxicity metrics rely on encoder models trained on specific toxicity datasets, which are susceptible to out-of-distribution (OOD) problems and depend on the dataset’s definition of toxicity. |
| Approach: | They propose a robust metric grounded on LLMs to flexibly measure toxicity according to the given definition by analysing toxicity factors and intrinsic toxic attributes. |
| Outcome: | The proposed metric improves on conventional metrics by 12 points in the F1 score and shows that upstream toxicity significantly influences downstream metrics, suggesting that LLMs are unsuitable for toxicity evaluations within unverified factors. |
Return of EM: Entity-driven Answer Set Expansion for QA Evaluation (2025.coling-main)
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| Challenge: | Recent studies show that using large language models (LLMs) is the most reliable method to evaluate QA models, but suffers from limited interpretability, high cost, and environmental harm. |
| Approach: | They propose to use soft exact match (EM) with entity-driven answer set expansion to expand gold answer set to include diverse surface forms. |
| Outcome: | The proposed method outperforms traditional evaluation methods while offering the benefits of high interpretability and reduced environmental harm. |
Analyzing Norm Violations in Live-Stream Chat (2023.emnlp-main)
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Jihyung Moon, Dong-Ho Lee, Hyundong Cho, Woojeong Jin, Chan Park, Minwoo Kim, Jonathan May, Jay Pujara, Sungjoon Park
| Challenge: | Existing methods for detecting toxic language and norm violations are limited to live-streaming platforms . existing methods are less effective when applied to live streaming platforms based on a limited time frame . |
| Approach: | They propose to use contextual information to automatically moderate toxic content on live streaming platforms. |
| Outcome: | The proposed model improves on live-streaming platforms by 35%. |
Asking Clarification Questions to Handle Ambiguity in Open-Domain QA (2023.findings-emnlp)
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| Challenge: | Ambiguous questions persist in open-domain question answering because formulating a precise question with a unique answer is often challenging. |
| Approach: | They propose to ask a clarification question where the user’s response will help identify the interpretation that best aligns with the user's intention. |
| Outcome: | The proposed approach achieves F1 of 61.3, 25.1, and 40.5 on the three tasks, demonstrating the need for further improvements while providing competitive baselines for future work. |
Puzzled by Puzzles: When Vision-Language Models Can’t Take a Hint (2025.emnlp-main)
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| Challenge: | rebus puzzles encode language through imagery, spatial arrangement, and symbolic substitution. |
| Approach: | They construct a benchmark of rebus puzzles in english language to test their ability to interpret and solve them. |
| Outcome: | The proposed model performs well on a set of english-language rebus puzzles. |
KorNAT: LLM Alignment Benchmark for Korean Social Values and Common Knowledge (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) must possess an understanding of the nation’s culture and basic knowledge. |
| Approach: | They propose to construct a national alignment benchmark, KorNAT, which measures the alignment between an LLM and a targeted country from two perspectives: social value alignment and common knowledge alignment. |
| Outcome: | The proposed model passes the national alignment score of 7 LLMs, indicating there is room for improvement. |
Fine-grained Gender Control in Machine Translation with Large Language Models (2024.naacl-long)
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| Challenge: | Existing work on controlled translation has only considered a simplified setup of one target gender for input. |
| Approach: | They propose a Gender-of-Entity prompting method for machine translation that takes the gender of the ambiguous entity as additional input and propose to use it to translate with correct gender inflections. |
| Outcome: | The proposed method instructs the model with fine-grained entity-level gender information to translate with correct gender inflections. |