Developing a Reliable, Fast, General-Purpose Hallucination Detection and Mitigation Service

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 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.