Developing a Reliable, General-Purpose Hallucination Detection and Mitigation Service: Insights and Lessons Learned

• URL: http://arxiv.org/abs/2407.15441v1
• Date: Mon, 22 Jul 2024 07:48:30 GMT
• Title: Developing a Reliable, General-Purpose Hallucination Detection and Mitigation Service: Insights and Lessons Learned
• Authors: Song Wang, Xun Wang, Jie Mei, Yujia Xie, Sean Muarray, Zhang Li, Lingfeng Wu, Si-Qing Chen, Wayne Xiong,
• Abstract summary: We introduce a reliable and high-speed production system aimed at detecting and rectifying the hallucination issue within large language models (LLMs) Our system encompasses named entity recognition (NER), natural language inference (NLI), span-based detection (SBD) We detail the core elements of our framework and underscore the paramount challenges tied to response time, availability, and performance metrics.
• Score: 36.216938133315786
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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