Papers with progressive alignment hypothesis

1 papers
Phased Instruction Fine-Tuning for Large Language Models (2024.findings-acl)

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Challenge: Existing methods to enhance pre-trained language models' ability to follow instructions are limited due to the simultaneous handling of varying instruction complexities.
Approach: They propose a phased instruction fine-tuning method that posits that the transition of a pre-trained language model from simple next-word prediction to sophisticated instruction following is a gradual learning process.
Outcome: The proposed method surpasses the one-off instruction fine-tuning method in win rate and validates the hypothesis of progressive alignment.

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