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General Direction for Pre-training: Scaling Simple Tasks
The success of models like RoBERTa suggests a general principle for advancing pre-trained models: continuous performance improvements can be achieved by scaling up the training process, using more data and compute, even on relatively simple pre-training objectives.
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
RoBERTa's Key Findings on Scaling
Impact of Removing NSP Loss in RoBERTa
General Direction for Pre-training: Scaling Simple Tasks
A research team is building a new language model for natural language understanding tasks. They have a fixed model architecture and a large computational budget. They are debating the most effective pre-training strategy. Based on the primary findings demonstrated by subsequent improvements on encoder-only models, which approach is most likely to yield the best performance?
Diagnosing Pre-training Issues in Large-Scale Models
Optimizing Pre-training Objectives for Large-Scale Models
Learn After
Evaluating a Language Model Pre-training Strategy
A research team is planning to pre-train a new language model with a fixed computational budget. One senior researcher argues, 'Instead of just using more data with our current simple masked language modeling objective, we should dedicate a significant portion of our budget to developing and implementing a novel, more complex pre-training task. This complexity is the key to unlocking better performance.' Based on the major findings from large-scale model training, which of the following statements provides the most accurate evaluation of this researcher's argument?
Prioritizing Pre-training Efforts