Papers by Yoonjoo Lee
Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM Reviews (2025.emnlp-main)
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Hyungyu Shin, Jingyu Tang, Yoonjoo Lee, Nayoung Kim, Hyunseung Lim, Ji Yong Cho, Hwajung Hong, Moontae Lee, Juho Kim
| Challenge: | Large Language Models (LLMs) can automatically draft reviews, but determining whether they are trustworthy requires systematic evaluation. |
| Approach: | They propose an automatic focus-level evaluation pipeline based on two sets of facets . authors evaluated LLM reviews at surface-level or content-level . |
| Outcome: | The proposed framework enables automatic evaluation of paper reviews based on two sets of facets . the framework compared open review paper reviews with human experts on validity, clarity, novelty . |
The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models (2025.naacl-long)
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Seungone Kim, Juyoung Suk, Ji Yong Cho, Shayne Longpre, Chaeeun Kim, Dongkeun Yoon, Guijin Son, Yejin Cho, Sheikh Shafayat, Jinheon Baek, Sue Hyun Park, Hyeonbin Hwang, Jinkyung Jo, Hyowon Cho, Haebin Shin, Seongyun Lee, Hanseok Oh, Noah Lee, Namgyu Ho, Se June Joo, Miyoung Ko, Yoonjoo Lee, Hyungjoo Chae, Jamin Shin, Joel Jang, Seonghyeon Ye, Bill Yuchen Lin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, Minjoon Seo
| Challenge: | a recent study evaluated language models using abstract evaluation criteria that lack the flexibility and granularity of human assessment. |
| Approach: | They propose a benchmark to evaluate nine distinct language models' capabilities . they use instance-specific evaluation criteria to mirror human evaluation . |
| Outcome: | The proposed benchmark evaluates nine distinct capabilities of language models across 77 tasks. |
ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models (2024.emnlp-main)
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Benjamin Newman, Yoonjoo Lee, Aakanksha Naik, Pao Siangliulue, Raymond Fok, Juho Kim, Daniel Weld, Joseph Chee Chang, Kyle Lo
| Challenge: | Using language models (LMs) can generate literature review tables by decomposing it into separate schema and value generation steps. |
| Approach: | They propose a framework that leverages language models to perform literature review table generation by decomposing it into separate schema and value generation steps. |
| Outcome: | The proposed framework decomposes the task into two sub-tasks: schema generation and value generation. |