Papers by Sue Black

1 papers
Self-Regulated Sample Diversity in Large Language Models (2024.findings-naacl)

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Challenge: Existing methods that require expensive setups or maintain static values during inference are inflexible and require expensive training.
Approach: They propose a self-regulating approach that adjusts sample diversity parameters dynamically based on the input prompt.
Outcome: The proposed method significantly improves the quality of responses generically without model retraining or fine-tuning.

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