RAG LLM Hallucination Detection Using VAE Latent-Space Validation
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Solution Overview
Problem
Retrieval-Augmented Generation (RAG) enhanced Large Language Models (LLMs) suffer from AI-generated hallucinations, leading to incorrect, illogical, or factually incoherent outputs, which undermines their reliability and utility in critical applications.
Innovation Solution
Implement a Variational Autoencoder (VAE) to characterize the distribution of normal and hallucination outputs in a latent space, using a training dataset labeled by domain experts, and compare encoder output vectors to detect candidate hallucinations by measuring distance metrics or applying thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If RAG models incorporate external knowledge bases to enhance contextual relevance and factual accuracy, then the reliability of outputs is improved, but hallucinations (incorrect, illogical, or factually incoherent outputs) still persist and undermine credibility
Solution Approach 1:
A Variational Autoencoder (VAE) is introduced as an intermediary system between the RAG model and the output. The VAE learns the distribution of normal outputs during training and uses this learned distribution to detect and flag potential hallucinations, acting as a mediator that filters and validates the RAG model's outputs without modifying the RAG model itself
Solution Approach 2:
The system implements feedback by comparing the encoder output vector against the learned characteristic distribution of normal outputs. When deviations are detected, the system provides feedback signals that indicate potential hallucinations, enabling continuous monitoring and validation of output quality
2Measurement precision
If a Variational Autoencoder is trained to detect hallucinations by comparing encoder output vectors with learned distributions, then hallucination detection accuracy is improved, but the system complexity increases
Solution Approach 1:
The system transforms the hallucination detection problem into a different dimension by using the VAE's latent space representation. Instead of analyzing outputs directly in the original space, the encoder maps outputs to a compressed latent representation, and detection is performed by comparing these latent vectors against the learned distribution, effectively solving the problem in a transformed dimensional space
Data Source
AI summary
The disclosure provides runtime and training methods, computing apparatus, and computer readable media for use in detecting candidate hallucinations in outputs of a Retrieval-Augmented Generation (RAG) enhanced Large Language Model (LLM) trained to retrieve documents from a closed domain knowledge base responsive to an input query, and generate an LLM output vector based on the query and any retrieved documents. The method includes inputting an LLM output vector received from an LLM to an encoder part of a Variational Autoencoder and receiving from an encoder output layer thereof an encoder output vector having values representing a distribution of the LLM output vector in a dimensionally reduced latent space. The Variational Autoencoder is trained using a training dataset of LLM output vectors generated by the LLM labelled as normal outputs of the LLM or hallucination outputs of the LLM.