The key insight underlying RESPECT is that conversational implicit feedback signals occupy a relatively constrained subspace of natural language. Such signals can include direct approvals or signs of frustration, and also more subtle cues, such as when the user rephrases their request.
This novel approach, dubbed RESPECT, shows promise for creating AI systems that can continually learn and adapt without the need for extensive external annotations or feedback.
Through thousands of interactions with human users, the AI system was able to dramatically improve its task completion rate from an initial 31% to an impressive 82% - all without any explicit feedback or additional training data.
The RESPECT method allows AI to learn from natural human conversations, interpreting implicit responses to enhance performance on complex tasks without the usual need for explicit directions.
#natural-language-processing #learning-algorithms #cornell-university-research #human-computer-interaction
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