Related papers: Trapping LLM Hallucinations Using Tagged Context Prompts

Trapping LLM Hallucinations Using Tagged Context Prompts

URL: http://arxiv.org/abs/2306.06085v1
Date: Fri, 9 Jun 2023 17:48:54 GMT
Title: Trapping LLM Hallucinations Using Tagged Context Prompts
Authors: Philip Feldman, James R. Foulds, Shimei Pan
Abstract summary: We propose a novel method to recognize and flag instances when large language models perform outside their domain knowledge. We find that the use of context combined with embedded tags can successfully combat hallucinations within generative language models.
Score: 11.655802601887197
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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. We find that the use of context combined with embedded tags can successfully combat hallucinations within generative language models. To do this, we baseline hallucination frequency in no-context prompt-response pairs using generated URLs as easily-tested indicators of fabricated data. We observed a significant reduction in overall hallucination when context was supplied along with question prompts for tested generative engines. Lastly, we evaluated how placing tags within contexts impacted model responses and were able to eliminate hallucinations in responses with 98.88% effectiveness.

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