Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Kayi, Efsun"'
Autor:
Mayfield, James, Yang, Eugene, Lawrie, Dawn, MacAvaney, Sean, McNamee, Paul, Oard, Douglas W., Soldaini, Luca, Soboroff, Ian, Weller, Orion, Kayi, Efsun, Sanders, Kate, Mason, Marc, Hibbler, Noah
Large Language Models (LLMs) have enabled new ways to satisfy information needs. Although great strides have been made in applying them to settings like document ranking and short-form text generation, they still struggle to compose complete, accurat
Externí odkaz:
http://arxiv.org/abs/2405.00982
PLAID, an efficient implementation of the ColBERT late interaction bi-encoder using pretrained language models for ranking, consistently achieves state-of-the-art performance in monolingual, cross-language, and multilingual retrieval. PLAID differs f
Externí odkaz:
http://arxiv.org/abs/2405.00975
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
Autor:
Chaudhary, Aditi, Dalmia, Siddharth, Hu, Junjie, Li, Xinjian, Matthews, Austin, Muis, Aldrian Obaja, Otani, Naoki, Rijhwani, Shruti, Sheikh, Zaid, Vyas, Nidhi, Wang, Xinyi, Xie, Jiateng, Xu, Ruochen, Zhou, Chunting, Jansen, Peter J., Yang, Yiming, Levin, Lori, Metze, Florian, Mitamura, Teruko, Mortensen, David R., Neubig, Graham, Hovy, Eduard, Black, Alan W, Carbonell, Jaime, Horwood, Graham V., Tafreshi, Shabnam, Diab, Mona, Kayi, Efsun S., Farra, Noura, McKeown, Kathleen
This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech
Externí odkaz:
http://arxiv.org/abs/1902.08899
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