Papers by Taehee Lee
Leveraging What’s Overfixed: Post-Correction via LLM Grammatical Error Overcorrection (2025.emnlp-main)
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| Challenge: | Existing methods to reduce overcorrection often result in significantly decreased recall, limiting the usability of correction systems. |
| Approach: | They propose a novel approach that leverages the strengths of large language models to balance recall and precision by triggering overcorrection via LLMs and fine-tuning smaller models to identify and refine erroneous outputs. |
| Outcome: | The proposed approach maximizes recall and precision by leveraging the generative power of LLMs while preserving the reliability of smaller supervised models. |
Cluster-Guided Label Generation in Extreme Multi-Label Classification (2023.eacl-main)
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| Challenge: | Existing classification-based models are poorly per-form for tail labels and ignore semantic relations among labels. |
| Approach: | They propose to guide label generation using label cluster information to hierarchically generate lower-level labels. |
| Outcome: | The proposed model outperforms classification and generation baselines on tail labels and improves in four popular XMC benchmarks. |
PePe: Personalized Post-editing Model utilizing User-generated Post-edits (2023.findings-eacl)
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| Challenge: | Existing neural machine translation models ignore personal style in their translations, but in these studies the definition of personal style is over-simplified. |
| Approach: | They propose a personalized automatic post-editing framework that generates sentences considering distinct personal behaviors by collecting post-edited data from a live machine translation system and combining a discriminator module and user-specific parameters. |
| Outcome: | The proposed model outperforms baseline models on four different metrics including BLEU, TER, YiSi-1, and human evaluation. |
EnSToM: Enhancing Dialogue Systems with Entropy-Scaled Steering Vectors for Topic Maintenance (2025.findings-acl)
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| Challenge: | Small large language models (sLLMs) are lightweight and efficient, but struggle to maintain topic consistency in task-oriented dialogue systems. |
| Approach: | They propose an approach to ensure topic consistency in task-oriented dialogue systems by manipulating internal activations during inference. |
| Outcome: | The proposed approach achieves significant performance gain with a relatively small data size compared to fine-tuning approaches. |
Polishing Every Facet of the GEM: Testing Linguistic Competence of LLMs and Humans in Korean (2025.acl-long)
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| Challenge: | Existing studies have focused on linguistic competence of language models with grammatical knowledge. |
| Approach: | They propose to use grammar as a measurable proxy to assess linguistic competence of large language models (LLMs) . |
| Outcome: | The proposed model aims to assess the linguistic competence of large language models (LLMs) and humans in Korean. |
Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment (2025.naacl-long)
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Sangwon Yu, Jongyoon Song, Bongkyu Hwang, Hoyoung Kang, Sooah Cho, Junhwa Choi, Seongho Joe, Taehee Lee, Youngjune Gwon, Sungroh Yoon
| Challenge: | Experimental results show that large language models exhibit a negative bias in binary decision tasks . hallucination is a factor that degrades reliability of LLMs . |
| Approach: | They propose a negative attention score to systematically and quantitatively formulate negative bias by using a parameter-efficient fine-tuning technique. |
| Outcome: | The proposed method reduces the gap between precision and recall caused by negative bias while preserving generalization abilities. |
Towards Prompt Generalization: Grammar-aware Cross-Prompt Automated Essay Scoring (2025.findings-naacl)
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| Challenge: | Existing approaches to score essays on unseen prompts are challenging to use in educational situations. |
| Approach: | They propose a grammar-aware cross-prompt trait scoring model which internally captures prompt-independent syntactic aspects to learn generic essay representation. |
| Outcome: | Empirical results show that the proposed model improves prompt-independent and grammar-related traits and achieves notable QWK gains in the most challenging cross-prompt scenario. |