"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis cover image

The Future of AI Security with Adam Wenchel, CEO of Arthur.ai

"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis

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Providing Metrics on Hallucinations and Optimizing Context Window in Language Models

One technique for automatically identifying hallucinations in language models is by breaking down the response into claims and determining if each claim is supported, contradicted, or not supported by the data. This approach has achieved about 87% accuracy in detecting hallucinations. By analyzing the data passed in and providing feedback, sophisticated metrics can be generated to measure rates of hallucinations. Optimizing the context window is another important aspect, where a balance needs to be found between providing enough information to avoid hallucinations and avoiding high costs and latency. Finding the right amount of historical interactions to feed into the language model also plays a role in optimizing performance.

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