Papers with CONSCENDI

1 papers
CONSCENDI: A Contrastive and Scenario-Guided Distillation Approach to Guardrail Models for Virtual Assistants (2024.naacl-long)

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Challenge: A major challenge in deploying LLM-based virtual conversational assistants in real world settings is ensuring they operate within what is admissible for the task.
Approach: They propose to use large language models (LLMs) to generate training data with two key LLM components: scenario-augmented generation and contrastive training examples.
Outcome: The proposed model improves over baselines in multiple dialogue domains.

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