Papers by Taehee Park
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. |
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. |
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. |