Developing a Reliable, Fast, General-Purpose Hallucination Detection and Mitigation Service
This addresses the challenge of ensuring accuracy and dependability in LLM applications, though it appears incremental as it builds on existing techniques like NER and NLI.
The paper tackles the problem of hallucination in large language models by introducing a detection and mitigation system, achieving reliable performance in evaluations with offline data and live production traffic.
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 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.