| Codegen: An open large language model for code with multi-turn program synthesis E Nijkamp, B Pang, H Hayashi, L Tu, H Wang, Y Zhou, S Savarese, ... arXiv preprint arXiv:2203.13474, 2022 | 1041 | 2022 |
| Commonsense knowledge base completion X Li, A Taheri, L Tu, K Gimpel Proceedings of the 54th Annual Meeting of the Association for Computational …, 2016 | 213 | 2016 |
| An empirical study on robustness to spurious correlations using pre-trained language models L Tu, G Lalwani, S Gella, H He Transactions of the Association for Computational Linguistics 8, 621-633, 2020 | 193 | 2020 |
| Pay attention to the ending: Strong neural baselines for the roc story cloze task Z Cai, L Tu, K Gimpel Proceedings of the 55th Annual Meeting of the Association for Computational …, 2017 | 74 | 2017 |
| Learning approximate inference networks for structured prediction L Tu, K Gimpel arXiv preprint arXiv:1803.03376, 2018 | 67 | 2018 |
| ENGINE: Energy-based inference networks for non-autoregressive machine translation L Tu, RY Pang, S Wiseman, K Gimpel arXiv preprint arXiv:2005.00850, 2020 | 63 | 2020 |
| Xgen-7b technical report E Nijkamp, T Xie, H Hayashi, B Pang, C Xia, C Xing, J Vig, S Yavuz, ... arXiv preprint arXiv:2309.03450, 2023 | 30 | 2023 |
| Prompt-Tuning Can Be Much Better Than Fine-Tuning on Cross-lingual Understanding With Multilingual Language Models L Tu, C Xiong, Y Zhou arXiv preprint arXiv:2210.12360, 2022 | 21 | 2022 |
| Learning to embed words in context for syntactic tasks L Tu, K Gimpel, K Livescu arXiv preprint arXiv:1706.02807, 2017 | 20 | 2017 |
| Generating diverse story continuations with controllable semantics L Tu, X Ding, D Yu, K Gimpel arXiv preprint arXiv:1909.13434, 2019 | 19 | 2019 |
| Benchmarking approximate inference methods for neural structured prediction L Tu, K Gimpel arXiv preprint arXiv:1904.01138, 2019 | 19 | 2019 |
| Quality signals in generated stories M Sagarkar, J Wieting, L Tu, K Gimpel Proceedings of the Seventh Joint Conference on Lexical and Computational …, 2018 | 16 | 2018 |
| Long sequence modeling with xgen: A 7b llm trained on 8k input sequence length E Nijkamp, T Xie, H Hayashi, B Pang, C Xia, C Xing, J Vig, S Yavuz, ... Salesforce AI Research Blog, 2023 | 15 | 2023 |
| Improving joint training of inference networks and structured prediction energy networks L Tu, RY Pang, K Gimpel arXiv preprint arXiv:1911.02891, 2019 | 15 | 2019 |
| AugTriever: Unsupervised Dense Retrieval by Scalable Data Augmentation R Meng, Y Liu, S Yavuz, D Agarwal, L Tu, N Yu, J Zhang, M Bhat, Y Zhou | 9* | |
| Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning L Tu, J Qu, S Yavuz, S Joty, W Liu, C Xiong, Y Zhou arXiv preprint arXiv:2304.01295, 2023 | 7 | 2023 |
| An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks L Tu, T Liu, K Gimpel arXiv preprint arXiv:2010.02789, 2020 | 4 | 2020 |
| xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations C Qin, C Xia, K Ramakrishnan, M Ryoo, L Tu, Y Feng, M Shu, H Zhou, ... arXiv preprint arXiv:2408.12590, 2024 | 2 | 2024 |
| Unlocking Anticipatory Text Generation: A Constrained Approach for Faithful Decoding with Large Language Models L Tu, S Yavuz, J Qu, J Xu, R Meng, C Xiong, Y Zhou arXiv preprint arXiv:2312.06149, 2023 | 1 | 2023 |
| Learning Energy-Based Approximate Inference Networks for Structured Applications in NLP L Tu arXiv preprint arXiv:2108.12522, 2021 | 1 | 2021 |
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