Papers by Sanwoo Lee
Unleashing Large Language Models’ Proficiency in Zero-shot Essay Scoring (2024.findings-emnlp)
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| Challenge: | Recent advances in automated essay scoring (AES) have relied on labeled essays, requiring tremendous cost and expertise for their acquisition. |
| Approach: | They propose a zero-shot prompting framework that automatically decomposes writing proficiency into distinct traits and generates scoring criteria for each trait. |
| Outcome: | The proposed framework outperforms straightforward prompting (Vanilla) on TOEFL11 and ASAP, while the small-sized Llama2-13b-chat significantly outperformed ChatGPT. |
3DM: Distill, Dynamic Drop, and Merge for Debiasing Multi-modal Large Language Models (2025.findings-acl)
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| Challenge: | Recent advances in Multi-modal Language Models have shown remarkable performance in multimodal tasks . however, these models often exhibit inherent biases that compromise their reliability and fairness. |
| Approach: | They propose a framework that integrates Distill, Dynamic Drop, and Merge to address these challenges. |
| Outcome: | The proposed framework outperforms existing methods in balancing debiasing and improving performance on the MMSD2.0 sarcasm detection dataset. |
FPT: Feature Prompt Tuning for Few-shot Readability Assessment (2024.naacl-long)
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| Challenge: | Prompt-based methods lack crucial linguistic knowledge for readability assessment tasks such as word length, sentence length, and usage of different difficulty-level words. |
| Approach: | They propose a new prompt-based tuning framework that incorporates linguistic knowledge and a loss function to calibrate the similarity ranking order between categories. |
| Outcome: | The proposed framework outperforms the large language model gpt-3.5-turbo-16k in most cases. |
Composable Cross-prompt Essay Scoring by Merging Models (2025.emnlp-main)
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| Challenge: | Existing approaches to cross-prompt automated essay scoring use all available sources . however, using multiple sources for continual adaptation raises privacy concerns . |
| Approach: | They propose a source-free adaptation approach that selectively merges the parameters of individual models without further access to the source datasets. |
| Outcome: | The proposed method outperforms joint-training methods on all sources while maintaining computational efficiency. |
Dynamic Fisher-weighted Model Merging via Bayesian Optimization (2025.naacl-long)
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| Challenge: | Existing merging approaches involve scaling the parameters model-wise or integrating parameter importance parameter-wise. |
| Approach: | They propose a method for merging model-based models at the parameter level without training data or joint training. |
| Outcome: | The proposed model merging framework outperforms baseline models on validation sets. |