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In Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated high text generation quality.
Parameter-Efficient Transfer Learning for NLP
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TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification. In Findings of the Association for Computational Linguistics: EMNLP 2020 , Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, Online, 1644–1650
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PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, Online, 781–793
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MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, Online, 2831–2845
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A Pre-Training Based Personalized Dialogue Generation Model with Persona-Sparse Data
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DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION. In International Conference on Learning Representations
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Director: Generator-Classifiers For Supervised Language Modeling. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , Yulan He, Heng Ji, Sujian Li, Yang Liu, and Chua-Hui Chang (Eds.). Association for Computational Linguistics, Online only, 512–526
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Constitutional AI: Harmlessness from AI Feedback
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Style Locality for Controllable Generation with kNN Language Models. In Proceedings of the 1st Workshop on Taming Large Language Models: Controllability in the era of Interactive Assistants! , Devamanyu Hazarika, Xiangru Robert Tang, and Di Jin (Eds.). Association for Computational Linguistics, Prague, Czech Republic, 68–75
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LLaMA: Open and Efficient Foundation Language Models
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