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The in-context learning ability of large language models (LLMs) enables them to generalize to novel downstream tasks with relatively few labeled examples.
Towards scalable multi-domain conversational agents: The schema-guided dialogue dataset, 2019
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Slurp: A spoken language understanding resource package, 2020
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Semi-supervised sequence learning, 2015
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Distilling the knowledge in a neural network
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Creating training corpora for nlg micro-planning
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Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Looking beyond the surface: A challenge set for reading comprehension over multiple sentences
Khashabi, D., Chaturvedi, S., Roth, M., Upadhyay, S., and Roth, D · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S · 2018
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Boolq: Exploring the surprising difficulty of natural yes/no questions
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K · 2019
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PubMedQA: A dataset for biomedical research question answering
Jin, Q., Dhingra, B., Liu, Z., Cohen, W., and Lu, X · 2019
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Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., and Zettlemoyer, L · 2019
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Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
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Superglue: A stickier benchmark for general-purpose language understanding systems
Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S · 2019
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Language models are few-shot learners
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The turking test: Can language models understand instructions?
Efrat, A. and Levy, O · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P. J., et al · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Rasley, J., Rajbhandari, S., Ruwase, O., and He, Y · 2020
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Inpars: Data augmentation for information retrieval using large language models, 2022
Bonifacio, L., Abonizio, H., Fadaee, M., and Nogueira, R · 2022
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Weakly supervised data augmentation through prompting for dialogue understanding
Chen, M., Papangelis, A., Tao, C., Rosenbaum, A., Kim, S., Liu, Y., Yu, Z., and Hakkani-Tur, D · 2022
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Is gpt-3 a good data annotator?, 2022
Ding, B., Qin, C., Liu, L., Bing, L., Joty, S., and Li, B · 2022
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Large language models are reasoning teachers
Ho, N., Schmid, L., and Yun, S.-Y · 2022
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Generating training data with language models: Towards zero-shot language understanding
Meng, Y., Huang, J., Zhang, Y., and Han, J · 2022
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GPT-NeoX: Large Scale Autoregressive Language Modeling in PyTorch, August 2021
Andonian, A., Biderman, S., Black, S., Gali, P., Gao, L., Hallahan, E., Levy-Kramer, J., Leahy, C., Nestler, L., Parker, K., Pieler, M., Purohit, S., Songz, T., Phil, W., and Weinbach, S · 2021
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The gem benchmark: Natural language generation, its evaluation and metrics
Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Anuoluwapo, A., Bosselut, A., Chandu, K. R., Clinciu, M., Das, D., Dhole, K. D., et al · 2021
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Generating datasets with pretrained language models, 2021
Schick, T. and Schütze, H · 2021
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Want to reduce labeling cost? gpt-3 can help, 2021
Wang, S., Liu, Y., Xu, Y., Zhu, C., and Zeng, M · 2021
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Gpt3mix: Leveraging large-scale language models for text augmentation
Yoo, K. M., Park, D., Kang, J., Lee, S.-W., and Park, W · 2021
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Explicit knowledge transfer for weakly-supervised code generation, 2022
Azerbayev, Z., Ni, A., Schoelkopf, H., and Radev, D · 2022
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A survey on data augmentation for text classification
Bayer, M., Kaufhold, M.-A., and Reuter, C · 2022
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Data augmentation for intent classification with off-the-shelf large language models
Sahu, G., Rodriguez, P., Laradji, I. H., Atighehchian, P., Vazquez, D., and Bahdanau, D · 2022
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Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks, 2022
Wang, Y., Mishra, S., Alipoormolabashi, P., Kordi, Y., Mirzaei, A., Arunkumar, A., Ashok, A., Dhanasekaran, A. S., Naik, A., Stap, D., Pathak, E., Karamanolakis, G., Lai, H. G., Purohit, I., Mondal, I., Anderson, J., Kuznia, K., Doshi, K., Patel, M., Pal, K. K., Moradshahi, M., Parmar, M., Purohit, M., Varshney, N., Kaza, P. R., Verma, P., Puri, R. S., Karia, R., Sampat, S. K., Doshi, S., Mishra, S., Reddy, S., Patro, S., Dixit, T., Shen, X., Baral, C., Choi, Y., Smith, N. A., Hajishirzi, H., and Khashabi, D · 2022
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Emergent abilities of large language models
Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., et al · 2022
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URL https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
Open LLM Leaderboard - a Hugging Face Space by HuggingFaceH4, 2023 · 2023
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Gunasekar, S., Zhang, Y., Aneja, J., Mendes, C. C. T., Del Giorno, A., Gopi, S., Javaheripi, M., Kauffmann, P., de Rosa, G., Saarikivi, O., et al · 2023
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Challenges and Applications of Large Language Models, July 2023
Kaddour, J., Harris, J., Mozes, M., Bradley, H., Raileanu, R., and McHardy, R · 2023
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Textbooks are all you need ii: phi-1.5 technical report
Li, Y., Bubeck, S., Eldan, R., Del Giorno, A., Gunasekar, S., and Lee, Y. T · 2023
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Augesc: Dialogue augmentation with large language models for emotional support conversation, 2023
Zheng, C., Sabour, S., Wen, J., Zhang, Z., and Huang, M · 2023
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