Developing a Reliable, Fast, General-Purpose Hallucination Detection and Mitigation Service
Song Wang, Xun Wang, Jie Mei, Yujia Xie, Sean Muarray, Zhang Li,, Lingfeng Wu, Si-Qing Chen, Wayne Xiong

TL;DR
This paper presents a fast, reliable system for detecting and mitigating hallucinations in large language models, combining multiple detection methods and a rewriting mechanism to improve factual accuracy in real-world applications.
Contribution
The paper introduces a comprehensive, high-speed hallucination detection and mitigation framework integrating NER, NLI, span-based detection, and decision trees, tailored for production deployment.
Findings
Effective detection of diverse hallucinations in LLM responses
Maintains a balance between precision, response time, and cost
Validated through offline and live traffic evaluations
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, we have 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…
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Taxonomy
TopicsMental Health Treatment and Access · Mental Health and Psychiatry · Epilepsy research and treatment
Methodstravel james
