Superficial Alignment Hypothesis
The superficial alignment hypothesis is a theory suggesting that an LLM's fundamental knowledge and capabilities are almost entirely established during pre-training. According to this view, the fine-tuning phase does not add significant new knowledge but rather performs a 'superficial' adjustment, aligning the model's existing abilities with specific user needs and instruction formats. This explains why alignment can be achieved with a relatively small amount of fine-tuning data and effort.
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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
Related
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
Implicit Learning of Instruction-Response Mappings During Pre-training
Explaining the Impact of Targeted Training
Learn After
Interpreting LLM Training Observations
A research team observes that a large language model, pre-trained on a massive text corpus, requires a surprisingly small dataset of instruction-following examples to become a helpful assistant. According to the Superficial Alignment Hypothesis, what is the most accurate explanation for this observation?
Explaining the Role of Fine-Tuning