Implicit Learning of Instruction-Response Mappings During Pre-training
A key perspective in language model development is that some instruction-response behaviors may already be learned and encoded by the model during its pre-training phase, before any targeted fine-tuning occurs.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Fine-Tuning Pre-trained Models for Downstream Tasks
Instruction Fine-Tuning
Superficial Alignment Hypothesis
Challenge of Opaque Pre-Training Data in Fine-Tuning
A team develops a large language model pre-trained on a massive, diverse corpus of text from the internet. When initially tested on the task of generating concise summaries of legal documents, its performance is poor and unstructured. The team then collects a small, curated dataset of 500 legal documents and their corresponding expert-written summaries. After training the model on this small dataset, its ability to summarize new legal documents improves dramatically. Which statement best analyze
Critiquing a Model Training Hypothesis
Explaining the Impact of Targeted Training
Learn After
Challenge of Opaque Pre-Training Data in Fine-Tuning
A machine learning engineer claims, "A language model's ability to follow instructions is exclusively a result of the targeted examples shown during its fine-tuning stage. The pre-training phase only provides it with general world knowledge and language structure."
Which of the following statements provides the most accurate evaluation of this claim?
Explaining Unexpected Model Capabilities
Explaining Emergent Zero-Shot Abilities