| Generative adversarial text to image synthesis S Reed, Z Akata, X Yan, L Logeswaran, B Schiele, H Lee International conference on machine learning, 1060-1069, 2016 | 4672 | 2016 |
| An efficient framework for learning sentence representations L Logeswaran, H Lee International Conference on Learning Representations, 2018 | 760 | 2018 |
| Knowledge unlearning for mitigating privacy risks in language models J Jang, D Yoon, S Yang, S Cha, M Lee, L Logeswaran, M Seo Proceedings of the 61st Annual Meeting of the Association for Computational …, 2023 | 385 | 2023 |
| Zero-shot entity linking by reading entity descriptions L Logeswaran, MW Chang, K Lee, K Toutanova, J Devlin, H Lee arXiv preprint arXiv:1906.07348, 2019 | 354 | 2019 |
| Sentence ordering and coherence modeling using recurrent neural networks L Logeswaran, H Lee, D Radev Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 151* | 2018 |
| Content preserving text generation with attribute controls L Logeswaran, H Lee, S Bengio Advances in Neural Information Processing Systems 31, 2018 | 148 | 2018 |
| When” a helpful assistant” is not really helpful: Personas in system prompts do not improve performances of large language models M Zheng, J Pei, L Logeswaran, M Lee, D Jurgens Findings of the Association for Computational Linguistics: EMNLP 2024, 15126 …, 2024 | 105 | 2024 |
| 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 | 97 | 2023 |
| Understanding the capabilities and limitations of large language models for cultural commonsense S Shen, L Logeswaran, M Lee, H Lee, S Poria, R Mihalcea arXiv preprint arXiv:2405.04655, 2024 | 74 | 2024 |
| Small language models need strong verifiers to self-correct reasoning Y Zhang, M Khalifa, L Logeswaran, J Kim, M Lee, H Lee, L Wang arXiv preprint arXiv:2404.17140, 2024 | 71 | 2024 |
| Autoguide: Automated generation and selection of context-aware guidelines for large language model agents Y Fu, DK Kim, J Kim, S Sohn, L Logeswaran, K Bae, H Lee Advances in Neural Information Processing Systems 37, 119919-119948, 2024 | 59* | 2024 |
| Dallas Card, and David Jurgens. 2024. You don’t need a personality test to know these models are unreliable: Assessing the reliability of large language models on psychometric … B Shu, L Zhang, M Choi, L Dunagan, L Logeswaran, M Lee Proceedings of the 2024 Conference of the North American Chapter of the …, 2023 | 52 | 2023 |
| Process reward models that think M Khalifa, R Agarwal, L Logeswaran, J Kim, H Peng, M Lee, H Lee, ... arXiv preprint arXiv:2504.16828, 2025 | 48 | 2025 |
| Merging generated and retrieved knowledge for open-domain QA Y Zhang, M Khalifa, L Logeswaran, M Lee, H Lee, L Wang arXiv preprint arXiv:2310.14393, 2023 | 45 | 2023 |
| Grace: Discriminator-guided chain-of-thought reasoning M Khalifa, L Logeswaran, M Lee, H Lee, L Wang arXiv preprint arXiv:2305.14934, 2023 | 43 | 2023 |
| Sprig: Improving large language model performance by system prompt optimization L Zhang, T Ergen, L Logeswaran, M Lee, D Jurgens arXiv preprint arXiv:2410.14826, 2024 | 37 | 2024 |
| Few-shot reranking for multi-hop QA via language model prompting M Khalifa, L Logeswaran, M Lee, H Lee, L Wang Proceedings of the 61st Annual Meeting of the Association for Computational …, 2023 | 30 | 2023 |
| Few-shot subgoal planning with language models L Logeswaran, Y Fu, M Lee, H Lee arXiv preprint arXiv:2205.14288, 2022 | 26 | 2022 |
| You don’t need a personality test to know these models are unreliable: Assessing the reliability of large language models on psychometric instruments B Shu, L Zhang, M Choi, L Dunagan, L Logeswaran, M Lee, D Card, ... Proceedings of the 2024 Conference of the North American Chapter of the …, 2024 | 22 | 2024 |
| Multimodal subtask graph generation from instructional videos Y Jang, S Sohn, L Logeswaran, T Luo, M Lee, H Lee arXiv preprint arXiv:2302.08672, 2023 | 18 | 2023 |