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09/08/2023
When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale
Large volumes of text data have contributed significantly to the develop...
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01/04/2023
InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval
Recently, InPars introduced a method to efficiently use large language m...
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12/12/2022
In Defense of Cross-Encoders for Zero-Shot Retrieval
Bi-encoders and cross-encoders are widely used in many state-of-the-art ...
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06/06/2022
No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrieval
Recent work has shown that small distilled language models are strong co...
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02/10/2022
InPars: Data Augmentation for Information Retrieval using Large Language Models
The information retrieval community has recently witnessed a revolution ...
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02/20/2021
Understanding and Enhancing the Use of Context for Machine Translation
To understand and infer meaning in language, neural models have to learn...
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10/30/2020
A New Neural Search and Insights Platform for Navigating and Organizing AI Research
To provide AI researchers with modern tools for dealing with the explosi...
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05/25/2020
The Unreasonable Volatility of Neural Machine Translation Models
Recent works have shown that Neural Machine Translation (NMT) models ach...
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08/27/2018
Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translation
Neural Machine Translation has achieved state-of-the-art performance for...
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02/13/2018
Examining the Tip of the Iceberg: A Data Set for Idiom Translation
Neural Machine Translation (NMT) has been widely used in recent years wi...
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05/01/2017
Learning Topic-Sensitive Word Representations
Distributed word representations are widely used for modeling words in N...
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05/01/2017
