| The cot collection: Improving zero-shot and few-shot learning of language models via chain-of-thought fine-tuning S Kim, SJ Joo, D Kim, J Jang, S Ye, J Shin, M Seo arXiv preprint arXiv:2305.14045, 2023 | 80 | 2023 |
| Flask: Fine-grained language model evaluation based on alignment skill sets S Ye, D Kim, S Kim, H Hwang, S Kim, Y Jo, J Thorne, J Kim, M Seo arXiv preprint arXiv:2307.10928, 2023 | 66 | 2023 |
| Exploring the benefits of training expert language models over instruction tuning J Jang, S Kim, S Ye, D Kim, L Logeswaran, M Lee, K Lee, M Seo International Conference on Machine Learning, 14702-14729, 2023 | 64 | 2023 |
| Selfee: Iterative self-revising llm empowered by self-feedback generation S Ye, Y Jo, D Kim, S Kim, H Hwang, M Seo Blog post, 2023 | 57 | 2023 |
| Guess the instruction! flipped learning makes language models stronger zero-shot learners S Ye, D Kim, J Jang, J Shin, M Seo arXiv preprint arXiv:2210.02969, 2022 | 35 | 2022 |
| Efficiently Enhancing Zero-Shot Performance of Instruction Following Model via Retrieval of Soft Prompt S Ye, J Jang, D Kim, Y Jo, M Seo Findings of the Association for Computational Linguistics: EMNLP 2023, 12288 …, 2023 | 12* | 2023 |
| Self-Explore: Enhancing Mathematical Reasoning in Language Models with Fine-grained Rewards H Hwang, D Kim, S Kim, S Ye, M Seo Findings of the Association for Computational Linguistics: EMNLP 2024, 1444-1466, 2024 | 9* | 2024 |
| How Well Do Large Language Models Truly Ground? H Lee, S Joo, C Kim, J Jang, D Kim, KW On, M Seo arXiv preprint arXiv:2311.09069, 2023 | 5 | 2023 |
| How language models extrapolate outside the training data: A case study in Textualized Gridworld D Kim, J Lee, J Park, M Seo arXiv preprint arXiv:2406.15275, 2024 | 2* | 2024 |
| Semiparametric Token-Sequence Co-Supervision H Lee, D Kim, J Jun, S Joo, J Jang, KW On, M Seo arXiv preprint arXiv:2403.09024, 2024 | | 2024 |
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