Papers by Dongkeun Yoon
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. |
LangBridge: Multilingual Reasoning Without Multilingual Supervision (2024.acl-long)
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| Challenge: | Existing approaches to adapt language models for multilingual reasoning tasks require multilingual supervision. |
| Approach: | They propose a zero-shot approach to adapt language models for multilingual reasoning tasks without multilingual supervision by bridging two models by introducing minimal trainable parameters between them. |
| Outcome: | The proposed approach significantly improves multilingual reasoning capabilities on low-resource languages. |
Gradient Ascent Post-training Enhances Language Model Generalization (2023.acl-short)
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| Challenge: | Recent studies show that language models can perform diverse downstream NLP tasks in a zero-shot manner without any gradient updates. |
| Approach: | They show that updating pretrained LMs with Gradient Ascent Post-training enhances zero-shot generalization capabilities without any task-specific fine-tuning. |
| Outcome: | The proposed method can be comparable to 2-3x times larger LMs across 12 different NLP tasks. |
Knowledge Unlearning for Mitigating Privacy Risks in Language Models (2023.acl-long)
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| Challenge: | Recent work shows that an adversary can extract training data from Pretrained Language Models including Personally Identifiable Information (PII) such as names, phone numbers, and email addresses. |
| Approach: | They propose to use knowledge unlearning to reduce privacy risks for LMs by performing gradient ascent on target token sequences instead of trying to unlearn all the data at once. |
| Outcome: | The proposed method can give a stronger empirical privacy guarantee in scenarios where the data vulnerable to extraction attacks are known a priori while being much more efficient and robust. |