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Self-Supervised Classification Tasks for Encoder Training
A common self-supervised learning strategy for training encoders involves formulating classification tasks. These tasks are designed by creating classification challenges directly from unlabeled text, providing the necessary supervision signals without manual annotation. There are numerous approaches to designing these self-supervised classification tasks.
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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
Comparison of Self-Supervised Pre-training and Self-Training
Architectural Categories of Pre-trained Transformers
Self-Supervised Classification Tasks for Encoder Training
Prefix Language Modeling (PrefixLM)
Mask-Predict Framework
Discriminative Training
Learning World Knowledge from Unlabeled Data
Emergent Linguistic Capabilities from Pre-training
Architectural Approaches to Self-Supervised Pre-training
Self-Supervised Pre-training of Encoders via Masked Language Modeling
Word Prediction as a Core Self-Supervised Task
Learning World Knowledge from Unlabeled Data via Self-Supervision
A research team has a massive collection of unlabeled historical texts. Their goal is to pre-train a language model that understands the specific vocabulary and sentence structures within these documents, but they have no budget for manual data annotation. Which of the following approaches is the most effective and feasible for their pre-training task?
Analysis of Supervision Signal Generation
A team is developing a pre-training strategy for a new language model using a large corpus of unlabeled text. Which of the following proposed tasks best exemplifies the principles of self-supervised learning?
Prevalence of Self-Supervised Pre-training in NLP
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Next Sentence Prediction (NSP)
Per-Token Classification for Encoder Training
Designing a Self-Supervised Text Classification Task
A researcher aims to pre-train a text encoder on a large corpus of unlabeled articles. They propose the following self-supervised classification task: For each training instance, a paragraph is extracted. With 50% probability, the sentences within that paragraph are randomly reordered. The model's task is to predict a binary label: 'Original Order' or 'Shuffled Order'. Which statement best evaluates the potential effectiveness of this task for its intended purpose?
A key aspect of training text encoders with self-supervision is designing a classification task that forces the model to learn a useful property of language. Match each proposed self-supervised classification task with the primary linguistic property it is designed to teach the model.