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Identifying high-value use cases for AI | Tomasz Tunguz (Founder of Theory Ventures)

Building With AI

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Controlling Error Rates in Large Language Models

Controlling error rates in large language models is crucial to avoid compounding mistakes. Without proper constraints on inputs and outputs, errors can quickly escalate and lead to nonsensical outcomes. One proposed solution involves large language models generating suggestions, which are then evaluated by classical machine learning models to identify valid versus invalid suggestions. By systematically quashing errors through this process, the overall output quality can be significantly improved.

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