Papers by Minjae Lee
Riemannian Optimization for LoRA on the Stiefel Manifold (2025.findings-emnlp)
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| Challenge: | powerful, large language models (LLMs) present significant fine-tuning challenges due to their size. |
| Approach: | They propose to optimize LoRA’s B matrix by imposing explicit orthogonality constraints that achieve near-perfect orthogonal and full effective rank. |
| Outcome: | The proposed method outperforms AdamW and LoRA in terms of parameter efficiency and representational capacity. |
A Testset for Context-Aware LLM Translation in Korean-to-English Discourse Level Translation (2025.coling-main)
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| Challenge: | Recent studies indicate that for high-resource languages, LLM surpasses encoder-decoder neural machine translation (NMT) models. |
| Approach: | They propose to construct a Korean-English discourse-level corpus with 600 text instances featuring six linguistic phenomena: lexical ambiguity, zero anaphora, slang, idiom, figurative language, and implicature. |
| Outcome: | The proposed corpus of 600 text instances features six linguistic phenomena, including lexical ambiguity, zero anaphora, slang, idiom, figurative language, and implicature. |
OASIS: Online Sample Selection for Continual Instruction Tuning (2026.acl-long)
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| Challenge: | Existing methods for continual instruction tuning (CIT) use pre-trained reference models, which are impractical in CIT setups since future data are unknown. |
| Approach: | They propose an adaptive online sample selection approach that estimates each sample’s informativeness relative to all previously seen data and minimizes informative redundancy through iterative selection score updates. |
| Outcome: | Experiments on various large foundation models show that using only 25% of the data achieves comparable performance to full-data training and outperforms the state-of-the-art sampling methods. |
ThinkBrake: Efficient Reasoning via Log-Probability Margin Guided Decoding (2026.findings-acl)
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| Challenge: | Recent advances in Large Reasoning Models (LRMs) have demonstrated remarkable capabilities across various tasks. |
| Approach: | They propose a system that stops reasoning when the margin between continuation token and lt;/think gt; narrows. |
| Outcome: | The proposed model reduces thinking token usage by 30% and improves accuracy by 8% while reducing thinking tokens by 72%. |
State-offset Tuning: State-based Parameter-Efficient Fine-Tuning for State Space Models (2025.acl-short)
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| Challenge: | State Space Models (SSMs) have emerged as efficient alternatives to Transformers, but their application to SSMs remains unexplored. |
| Approach: | They propose a state-based PEFT method that adjusts state directly instead of using external prompts. |
| Outcome: | The proposed method is based on state-offset tuning, which directly affects state at every timestep. |
LLM as a Risk Manager: LLM Semantic Filtering for Lead–Lag Trading in Prediction Markets (2026.acl-industry)
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Sumin Kim, Minjae Kim, Jihoon Kwon, Yoon Kim, Oscar Levy, Alejandro Lopez-Lira, Yongjae Lee, Chanyeol Choi
| Challenge: | Prediction markets provide a unique setting where event-level time series are directly tied to natural-language descriptions, yet discovering robust lead–lag relationships remains challenging due to spurious statistical correlations. |
| Approach: | They propose a statistical stage that uses Granger causality to identify candidate leader–follower pairs from market-implied probability time series and an LLM-based semantic stage that re-ranks these candidates by assessing whether the proposed direction admits a plausible economic transmission mechanism. |
| Outcome: | The proposed approach consistently outperforms the statistical baseline on Kalshi Economics markets. |
TABED: Test-Time Adaptive Ensemble Drafting for Robust Speculative Decoding in LVLMs (2026.findings-eacl)
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Minjae Lee, Wonjun Kang, Byeongkeun Ahn, Christian Classen, Kevin Galim, Seunghyuk Oh, Minghao Yan, Hyung Il Koo, Kangwook Lee
| Challenge: | Large Vision Language Models (LVLMs) are advanced models that process multiple modalities, such as images, audio, and video, alongside text. |
| Approach: | They propose to use a method to generate and verify draft tokens in parallel . they compare existing methods with small draft models and observe performance fluctuations . |
| Outcome: | The proposed method achieves an average walltime speedup of 1.74 over autoregressive decoding and a 5% improvement over single drafting methods. |