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pro vyhledávání: '"Fitzgerald, Jack"'
Autor:
Lawton, Neal, Padmakumar, Aishwarya, Gaspers, Judith, FitzGerald, Jack, Kumar, Anoop, Steeg, Greg Ver, Galstyan, Aram
QLoRA reduces the memory-cost of fine-tuning a large language model (LLM) with LoRA by quantizing the base LLM. However, quantization introduces quantization errors that negatively impact model performance after fine-tuning. In this paper we introduc
Externí odkaz:
http://arxiv.org/abs/2410.14713
Leveraging external knowledge is crucial for achieving high performance in knowledge-intensive tasks, such as question answering. The retrieve-and-read approach is widely adopted for integrating external knowledge into a language model. However, this
Externí odkaz:
http://arxiv.org/abs/2406.04670
Autor:
Ozdayi, Mustafa Safa, Peris, Charith, FitzGerald, Jack, Dupuy, Christophe, Majmudar, Jimit, Khan, Haidar, Parikh, Rahil, Gupta, Rahul
Large Language Models (LLMs) are known to memorize significant portions of their training data. Parts of this memorized content have been shown to be extractable by simply querying the model, which poses a privacy risk. We present a novel approach wh
Externí odkaz:
http://arxiv.org/abs/2305.11759
Publikováno v:
Proceedings of the Massively Multilingual Natural Language Understanding Workshop (MMNLU-22), pages 83 - 87 December 7, 2022, copyright 2022 Association for Computational Linguistics
Despite recent progress in Natural Language Understanding (NLU), the creation of multilingual NLU systems remains a challenge. It is common to have NLU systems limited to a subset of languages due to lack of available data. They also often vary widel
Externí odkaz:
http://arxiv.org/abs/2212.06346
Autor:
Soltan, Saleh, Ananthakrishnan, Shankar, FitzGerald, Jack, Gupta, Rahul, Hamza, Wael, Khan, Haidar, Peris, Charith, Rawls, Stephen, Rosenbaum, Andy, Rumshisky, Anna, Prakash, Chandana Satya, Sridhar, Mukund, Triefenbach, Fabian, Verma, Apurv, Tur, Gokhan, Natarajan, Prem
In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners than decoder-only models on various
Externí odkaz:
http://arxiv.org/abs/2208.01448
Autor:
FitzGerald, Jack, Ananthakrishnan, Shankar, Arkoudas, Konstantine, Bernardi, Davide, Bhagia, Abhishek, Bovi, Claudio Delli, Cao, Jin, Chada, Rakesh, Chauhan, Amit, Chen, Luoxin, Dwarakanath, Anurag, Dwivedi, Satyam, Gojayev, Turan, Gopalakrishnan, Karthik, Gueudre, Thomas, Hakkani-Tur, Dilek, Hamza, Wael, Hueser, Jonathan, Jose, Kevin Martin, Khan, Haidar, Liu, Beiye, Lu, Jianhua, Manzotti, Alessandro, Natarajan, Pradeep, Owczarzak, Karolina, Oz, Gokmen, Palumbo, Enrico, Peris, Charith, Prakash, Chandana Satya, Rawls, Stephen, Rosenbaum, Andy, Shenoy, Anjali, Soltan, Saleh, Sridhar, Mukund Harakere, Tan, Liz, Triefenbach, Fabian, Wei, Pan, Yu, Haiyang, Zheng, Shuai, Tur, Gokhan, Natarajan, Prem
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the N
Externí odkaz:
http://arxiv.org/abs/2206.07808
Autor:
FitzGerald, Jack, Hench, Christopher, Peris, Charith, Mackie, Scott, Rottmann, Kay, Sanchez, Ana, Nash, Aaron, Urbach, Liam, Kakarala, Vishesh, Singh, Richa, Ranganath, Swetha, Crist, Laurie, Britan, Misha, Leeuwis, Wouter, Tur, Gokhan, Natarajan, Prem
We present the MASSIVE dataset--Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation. MASSIVE contains 1M realistic, parallel, labeled virtual assistant utterances spanning 51 lang
Externí odkaz:
http://arxiv.org/abs/2204.08582
Autor:
Lee, Isabelle G., Zu, Vera, Buddi, Sai Srujana, Liang, Dennis, Kulkarni, Purva, Fitzgerald, Jack G. M.
Virtual Assistants can be quite literal at times. If the user says "tell Bob I love him," most virtual assistants will extract the message "I love him" and send it to the user's contact named Bob, rather than properly converting the message to "I lov
Externí odkaz:
http://arxiv.org/abs/2010.02600
Autor:
FitzGerald, Jack G. M.
Publikováno v:
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing (2020) 576-581
Slot-filling, Translation, Intent classification, and Language identification, or STIL, is a newly-proposed task for multilingual Natural Language Understanding (NLU). By performing simultaneous slot filling and translation into a single output langu
Externí odkaz:
http://arxiv.org/abs/2010.00760
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