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pro vyhledávání: '"Kayi, Efsun Sarioglu"'
While large language models (LLMs) are extremely capable at text generation, their outputs are still distinguishable from human-authored text. We explore this separation across many metrics over text, many sampling techniques, many types of text data
Externí odkaz:
http://arxiv.org/abs/2401.15476
Autor:
Reddy, Revanth Gangi, Sultan, Md Arafat, Kayi, Efsun Sarioglu, Zhang, Rong, Castelli, Vittorio, Sil, Avirup
Answer validation in machine reading comprehension (MRC) consists of verifying an extracted answer against an input context and question pair. Previous work has looked at re-assessing the "answerability" of the question given the extracted answer. He
Externí odkaz:
http://arxiv.org/abs/2011.03435
Autor:
Zhang, Rong, Reddy, Revanth Gangi, Sultan, Md Arafat, Castelli, Vittorio, Ferritto, Anthony, Florian, Radu, Kayi, Efsun Sarioglu, Roukos, Salim, Sil, Avirup, Ward, Todd
Transfer learning techniques are particularly useful in NLP tasks where a sizable amount of high-quality annotated data is difficult to obtain. Current approaches directly adapt a pre-trained language model (LM) on in-domain text before fine-tuning t
Externí odkaz:
http://arxiv.org/abs/2010.05904
Publikováno v:
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), Association for Computational Linguistics, 2017, pp. 241-250
Schizophrenia is one of the most disabling and difficult to treat of all human medical/health conditions, ranking in the top ten causes of disability worldwide. It has been a puzzle in part due to difficulty in identifying its basic, fundamental comp
Externí odkaz:
http://arxiv.org/abs/1810.09377
Electronic health records (EHRs) contain important clinical information about patients. Efficient and effective use of this information could supplement or even replace manual chart review as a means of studying and improving the quality and safety o
Externí odkaz:
http://arxiv.org/abs/1706.06177