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The emergence of Large Language Models (LLMs) has boosted performance and possibilities in various NLP tasks.
Fine-tuning language models from human preferences
Ziegler, D. M., Stiennon, N., Wu, J., Brown, T. B., Radford, A., Amodei, D., Christiano, P., and Irving, G. (2019) · 1909
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Binary codes capable of correcting deletions, insertions, and reversals
Levenshtein, V. I. (1966) · 1966
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Bleu: A method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J. (2002) · 2002
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ROUGE: A package for automatic evaluation of summaries
Lin, C.-Y. (2004) · 2004
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The history of information retrieval research
Sanderson, M. and Croft, W. B. (2012) · 2012
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Language-independent model for machine translation evaluation with reinforced factors
Han, L., Wong, D. F., Chao, L. S., He, L., Lu, Y., Xing, J., and Zeng, X. (2013) · 2013
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Squad: 100,000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P. (2016) · 2016
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Transkribus - a service platform for transcription, recognition and retrieval of historical documents
Kahle, P., Colutto, S., Hackl, G., and Mühlberger, G. (2017) · 2017
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Know what you don’t know: Unanswerable questions for squad
Rajpurkar, P., Jia, R., and Liang, P. (2018) · 2018
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Ocr-d: An end-to-end open source ocr framework for historical printed documents
Neudecker, C., Baierer, K., Federbusch, M., Boenig, M., Würzner, K.-M., Hartmann, V., and Herrmann, E. (2019) · 2019
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Generalizing question answering system with pre-trained language model fine-tuning
Su, D., Xu, Y., Winata, G. I., Xu, P., Kim, H., Liu, Z., and Fung, P. (2019) · 2019
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Goal-based evaluation of text mining results in an industrial use case
Drawehn, J., Blohm, M., Kintz, M., and Kochanowski, M. (2020) · 2020
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Make your Customers Happy Again. AI and NLP for a Customer Complaint Management Platform
Kintz, M., Dukino, C., Blohm, M., and Hanussek, M. (2020) · 2020
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Transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T. L., Gugger, S., Drame, M., Lhoest, Q., and Rush, A. M. (2020) · 2020
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cushlepor: customising hlepor metric using optuna for higher agreement with human judgments or pre-trained language model labse
Han, L., Sorokina, I., Erofeev, G., and Gladkoff, S. (2021) · 2021
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How can we know when language models know? on the calibration of language models for question answering
Jiang, Z., Araki, J., Ding, H., and Neubig, G. (2021) · 2021
Improving alignment of dialogue agents via targeted human judgements
Glaese, A., McAleese, N., Trebacz, M., Aslanides, J., Firoiu, V., Ewalds, T., Rauh, M., Weidinger, L., Chadwick, M., Thacker, P., Campbell-Gillingham, L., Uesato, J., Huang, P.-S., Comanescu, R., Yang, F., See, A., Dathathri, S., Greig, R., Chen, C., Fritz, D., Elias, J. S., Green, R., Mokrá, S., Fernando, N., Wu, B., Foley, R., Young, S., Gabriel, I., Isaac, W., Mellor, J., Hassabis, D., Kavukcuoglu, K., Hendricks, L. A., and Irving, G. (2022) · 2022
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Illustrating reinforcement learning from human feedback (rlhf)
Lambert, N., Castricato, L., von Werra, L., and Havrilla, A. (2022) · 2022
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Document ai (intelligent document processing)
Microsoft (2022) · 2022
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Gray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., and Lowe, R. (2022) · 2022
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Unifying vision, text, and layout for universal document processing
Tang, Z., Yang, Z., Wang, G., Fang, Y., Liu, Y., Zhu, C., Zeng, M., Zhang, C., and Bansal, M. (2022) · 2022
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A comparative study of transformer-based language models on extractive question answering
Pearce, K., Zhan, T., Komanduri, A., and Zhan, J. (2021) · 2021
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Tesseract ocr: an optical character recognition engine for various operating systems
Smith, R. (2019) · 2021
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Attention-guided generative models for extractive question answering
Xu, P., Liang, D., Huang, Z., and Xiang, B. (2021) · 2021
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Squad2.0: The stanford question answering dataset
Cloudera Fast Forward Labs (2020) · 2022
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Combining deep learning and reasoning for address detection in unstructured text documents
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A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity
Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., Do, Q. V., Xu, Y., and Fung, P. (2023) · 2023
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deepset (2023)
2023
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Pretraining language models with human preferences
Korbak, T., Shi, K., Chen, A., Bhalerao, R., Buckley, C. L., Phang, J., Bowman, S. R., and Perez, E. (2023) · 2023
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Are emergent abilities of large language models a mirage?
Schaeffer, R., Miranda, B., and Koyejo, S. (2023) · 2023
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A preliminary evaluation of chatgpt in requirements information retrieval
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