Výsledky vyhledávání - "Adams, Virginia"

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HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM

Autor: Wang, Zhilin, Dong, Yi, Zeng, Jiaqi, Adams, Virginia, Sreedhar, Makesh Narsimhan, Egert, Daniel, Delalleau, Olivier, Scowcroft, Jane Polak, Kant, Neel, Swope, Aidan, Kuchaiev, Oleksii

Existing open-source helpfulness preference datasets do not specify what makes some responses more helpful and others less so. Models trained on these datasets can incidentally learn to model dataset artifacts (e.g. preferring longer but unhelpful re

Externí odkaz: http://arxiv.org/abs/2311.09528

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Evaluating Parameter Efficient Learning for Generation

Autor: Xu, Peng, Patwary, Mostofa, Prabhumoye, Shrimai, Adams, Virginia, Prenger, Ryan J., Ping, Wei, Lee, Nayeon, Shoeybi, Mohammad, Catanzaro, Bryan

Parameter efficient learning methods (PERMs) have recently gained significant attention as they provide an efficient way for pre-trained language models (PLMs) to adapt to a downstream task. However, these conclusions are mostly drawn from in-domain

Externí odkaz: http://arxiv.org/abs/2210.13673

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Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation

Autor: Adams, Virginia, Subramanian, Sandeep, Chrzanowski, Mike, Hrinchuk, Oleksii, Kuchaiev, Oleksii

General translation models often still struggle to generate accurate translations in specialized domains. To guide machine translation practitioners and characterize the effectiveness of domain adaptation methods under different data availability sce

Externí odkaz: http://arxiv.org/abs/2206.01137

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Automatic Extraction of Medication Names in Tweets as Named Entity Recognition

Autor: Anderson, Carol, Liu, Bo, Abidin, Anas, Shin, Hoo-Chang, Adams, Virginia

Social media posts contain potentially valuable information about medical conditions and health-related behavior. Biocreative VII Task 3 focuses on mining this information by recognizing mentions of medications and dietary supplements in tweets. We a

Externí odkaz: http://arxiv.org/abs/2111.15641

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Chemical Identification and Indexing in PubMed Articles via BERT and Text-to-Text Approaches

Autor: Adams, Virginia, Shin, Hoo-Chang, Anderson, Carol, Liu, Bo, Abidin, Anas

The Biocreative VII Track-2 challenge consists of named entity recognition, entity-linking (or entity-normalization), and topic indexing tasks -- with entities and topics limited to chemicals for this challenge. Named entity recognition is a well-est

Externí odkaz: http://arxiv.org/abs/2111.15622

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Text Mining Drug/Chemical-Protein Interactions using an Ensemble of BERT and T5 Based Models

Autor: Adams, Virginia, Shin, Hoo-Chang, Anderson, Carol, Liu, Bo, Abidin, Anas

In Track-1 of the BioCreative VII Challenge participants are asked to identify interactions between drugs/chemicals and proteins. In-context named entity annotations for each drug/chemical and protein are provided and one of fourteen different intera

Externí odkaz: http://arxiv.org/abs/2111.15617

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NVIDIA NeMo Neural Machine Translation Systems for English-German and English-Russian News and Biomedical Tasks at WMT21

Autor: Subramanian, Sandeep, Hrinchuk, Oleksii, Adams, Virginia, Kuchaiev, Oleksii

This paper provides an overview of NVIDIA NeMo's neural machine translation systems for the constrained data track of the WMT21 News and Biomedical Shared Translation Tasks. Our news task submissions for English-German (En-De) and English-Russian (En

Externí odkaz: http://arxiv.org/abs/2111.08634

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