SLM Meets LLM: Balancing Latency, Interpretability and Consistency in Hallucination Detection
<|reference_start|>SLM Meets LLM: Balancing Latency, Interpretability and Consistency in Hallucination Detection: 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.<|reference_end|>
@article{hu2024slm,
    title={SLM Meets LLM: Balancing Latency, Interpretability and Consistency in
  Hallucination Detection},
    author={Mengya Hu and Rui Xu and Deren Lei and Yaxi Li and Mingyu Wang and
  Emily Ching and Eslam Kamal and Alex Deng},
    journal={arXiv preprint arXiv:2408.12748},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12748},
    primaryClass={cs.CL cs.AI cs.LG}
}