Papers by Jiwon Seo
LimaCost: Data Valuation for Instruction Tuning of Large Language Models (2025.findings-emnlp)
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| Challenge: | Instruction tuning is an effective approach for aligning large language models with human intentions. |
| Approach: | They propose a data quality measure that exhibits a strong correlation with model performance. |
| Outcome: | The proposed measure exhibits a strong correlation with model performance. |
Speculative Verification: Exploiting Information Gain for Speculative Decoding (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are used for many applications but their size and computational cost make inference serving a significant challenge. |
| Approach: | They propose an efficient augmentation to Speculative Decoding (SD) that predicts speculation accuracy and dynamically adapts the verification length to maximize throughput. |
| Outcome: | The proposed model reduces wasted verification on rejected tokens and improves decoding efficiency. |
X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents (2023.findings-acl)
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Mehrad Moradshahi, Tianhao Shen, Kalika Bali, Monojit Choudhury, Gael de Chalendar, Anmol Goel, Sungkyun Kim, Prashant Kodali, Ponnurangam Kumaraguru, Nasredine Semmar, Sina Semnani, Jiwon Seo, Vivek Seshadri, Manish Shrivastava, Michael Sun, Aditya Yadavalli, Chaobin You, Deyi Xiong, Monica Lam
| Challenge: | X-RiSAWOZ dataset has more than 18,000 human-verified dialogue utterances for each language . Xiaoping and Xinhui are the main challenges for task-oriented dialogue research . |
| Approach: | They develop a toolkit to accelerate the post-editing of a new language dataset after translation . their dataset, code, and toolkit are released open-source . |
| Outcome: | The proposed toolkit accelerates the post-editing of a new language dataset after translation. |