Related papers: SLM Meets LLM: Balancing Latency, Interpretability and Consistency in Hallucination Detection

SLM Meets LLM: Balancing Latency, Interpretability and Consistency in Hallucination Detection

URL: http://arxiv.org/abs/2408.12748v1
Date: Thu, 22 Aug 2024 22:13:13 GMT
Title: SLM Meets LLM: Balancing Latency, Interpretability and Consistency in Hallucination Detection
Authors: Mengya Hu, Rui Xu, Deren Lei, Yaxi Li, Mingyu Wang, Emily Ching, Eslam Kamal, Alex Deng,
Abstract summary: Large language models (LLMs) are highly capable but face latency challenges in real-time applications. This study optimize the real-time interpretable hallucination detection by introducing effective prompting techniques.
Score: 10.54378596443678
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: Large language models (LLMs) are highly capable but face latency challenges in real-time applications, such as conducting online hallucination detection. To overcome this issue, we propose a novel framework that leverages a small language model (SLM) classifier for initial detection, followed by a LLM as constrained reasoner to generate detailed explanations for detected hallucinated content. This study optimizes the real-time interpretable hallucination detection by introducing effective prompting techniques that align LLM-generated explanations with SLM decisions. Empirical experiment results demonstrate its effectiveness, thereby enhancing the overall user experience.

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