| 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 | 4321 | 2016 |
| An efficient framework for learning sentence representations L Logeswaran, H Lee International Conference on Learning Representations, 2018 | 725 | 2018 |
| 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 | 311 | 2019 |
| Knowledge unlearning for mitigating privacy risks in language models J Jang, D Yoon, S Yang, S Cha, M Lee, L Logeswaran, M Seo arXiv preprint arXiv:2210.01504, 2022 | 186 | 2022 |
| 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 | 143* | 2018 |
| Content preserving text generation with attribute controls L Logeswaran, H Lee, S Bengio Advances in Neural Information Processing Systems 31, 2018 | 138 | 2018 |
| 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 | 66 | 2023 |
| Grace: Discriminator-guided chain-of-thought reasoning M Khalifa, L Logeswaran, M Lee, H Lee, L Wang arXiv preprint arXiv:2305.14934, 2023 | 28 | 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 | 27 | 2024 |
| Autoguide: Automated generation and selection of state-aware guidelines for large language model agents Y Fu, DK Kim, J Kim, S Sohn, L Logeswaran, K Bae, H Lee arXiv e-prints, arXiv: 2403.08978, 2024 | 27* | 2024 |
| 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 | 24 | 2023 |
| Few-shot subgoal planning with language models L Logeswaran, Y Fu, M Lee, H Lee arXiv preprint arXiv:2205.14288, 2022 | 21 | 2022 |
| 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 | 19 | 2024 |
| Discriminator-guided multi-step reasoning with language models M Khalifa, L Logeswaran, M Lee, H Lee, L Wang arXiv preprint arXiv:2305.14934, 2023 | 16 | 2023 |
| Few-shot reranking for multi-hop qa via language model prompting M Khalifa, L Logeswaran, M Lee, H Lee, L Wang arXiv preprint arXiv:2205.12650, 2022 | 15 | 2022 |
| Performance, resource, and cost aware resource provisioning in the cloud L Logeswaran, HMND Bandara, HS Bhathiya 2016 IEEE 9th International Conference on Cloud Computing (CLOUD), 913-916, 2016 | 15 | 2016 |
| 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 | 12 | 2023 |
| Unsupervised task graph generation from instructional video transcripts L Logeswaran, S Sohn, Y Jang, M Lee, H Lee arXiv preprint arXiv:2302.09173, 2023 | 11 | 2023 |
| Few-shot sequence learning with transformers L Logeswaran, A Lee, M Ott, H Lee, MA Ranzato, A Szlam arXiv preprint arXiv:2012.09543, 2020 | 11 | 2020 |
| 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 | 6 | 2024 |
Honglak LeeLG AI Research / U. MichiganBestätigte E-Mail-Adresse bei umich.edu
