Developing a Reliable, General-Purpose Hallucination Detection and Mitigation Service: Insights and Lessons Learned
<|reference_start|>Developing a Reliable, General-Purpose Hallucination Detection and Mitigation Service: Insights and Lessons Learned: Hallucination, a phenomenon where large language models (LLMs) produce output that is factually incorrect or unrelated to the input, is a major challenge for LLM applications that require accuracy and dependability. In this paper, we introduce a reliable and high-speed production system aimed at detecting and rectifying the hallucination issue within LLMs. Our system encompasses named entity recognition (NER), natural language inference (NLI), span-based detection (SBD), and an intricate decision tree-based process to reliably detect a wide range of hallucinations in LLM responses. Furthermore, our team has crafted a rewriting mechanism that maintains an optimal mix of precision, response time, and cost-effectiveness. We detail the core elements of our framework and underscore the paramount challenges tied to response time, availability, and performance metrics, which are crucial for real-world deployment of these technologies. Our extensive evaluation, utilizing offline data and live production traffic, confirms the efficacy of our proposed framework and service.<|reference_end|>
@article{wang2024developing,
    title={Developing a Reliable, General-Purpose Hallucination Detection and
  Mitigation Service: Insights and Lessons Learned},
    author={Song Wang, Xun Wang, Jie Mei, Yujia Xie, Sean Muarray, Zhang Li,
  Lingfeng Wu, Si-Qing Chen, Wayne Xiong},
    journal={arXiv preprint arXiv:2407.15441},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15441},
    primaryClass={cs.CL}
}