Papers with Safer-Instruct framework

1 papers
Safer-Instruct: Aligning Language Models with Automated Preference Data (2024.naacl-long)

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Challenge: annotating preference data by humans is resource-intensive and creativity-demanding . existing methods face limitations in data diversity and quality .
Approach: They propose a pipeline for annotating large-scale preference data without human annotators.
Outcome: The proposed pipeline outperforms models fine-tuned on human-annotated safety preference data while maintaining a competitive edge in downstream tasks.

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