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2023

Trapping LLM Hallucinations Using Tagged Context Prompts

Feldman, Philip, Foulds, James R., Pan, Shimei

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Recent advances in large language models (LLMs), such as ChatGPT, have led to highly sophisticated conversation agents.

However, these models suffer from "hallucinations," where the model generates false or fabricated information.
Addressing this challenge is crucial, particularly with AI-driven platforms being adopted across various sectors.
In this paper, we propose a novel method to recognize and flag instances when LLMs perform outside their domain knowledge, and ensuring users receive accurate information.

Trapping LLM Hallucinations Using Tagged Context Prompts · Around

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