Hallucination Detection in LLMs: Fast and Memory-Efficient Finetuned Models
Gabriel Y. Arteaga, Thomas B. Schön, Nicolas Pielawski·September 04, 2024
Summary
A novel method for fast, memory-efficient training of Large Language Model (LLM) ensembles to detect hallucinations is introduced. The ensemble uses a shared matrix of pre-trained "slow weights" updated with Low-Rank Adaptation (LoRA) matrices during training. Each ensemble member combines with the shared weights using a Hadamard product. The ensemble generates uncertainty estimates, serving as features for a classifier to determine if the LLM's prediction is correct or hallucinated. This approach enables practical use with only one GPU required for training and inference, offering a viable solution for uncertainty estimation in LLMs.
Introduction
Background
Overview of Large Language Models (LLMs)
Importance of detecting hallucinations in LLMs
Objective
Aim of the novel method
Key benefits: speed, memory efficiency, and practicality
Method
Data Collection
Sources of data for training and testing
Data preprocessing steps
Data Preprocessing
Techniques for preparing data for the ensemble model
Handling of large datasets efficiently
Shared Matrix of Pre-Trained "Slow Weights"
Description of the shared matrix
Role in the ensemble architecture
Low-Rank Adaptation (LoRA) Matrices
Explanation of LoRA matrices
How they are used to update the shared weights
Ensemble Member Configuration
Structure of each ensemble member
Integration of shared weights using the Hadamard product
Uncertainty Estimation
Method for generating uncertainty estimates
Features extracted from the ensemble output
Classifier for Hallucination Detection
Description of the classifier
Training and evaluation of the classifier
Implementation
Hardware Requirements
Minimum hardware needed for training and inference
Scalability considerations
Training Process
Detailed steps for training the ensemble model
Optimization techniques for efficiency
Inference Process
Overview of the inference procedure
Efficiency gains compared to traditional methods
Evaluation
Metrics for Hallucination Detection
Key performance indicators
Comparison with existing methods
Case Studies
Real-world applications and results
Validation of the method's effectiveness
Conclusion
Summary of Contributions
Recap of the novel method's achievements
Future Work
Potential areas for further research
Open challenges in LLM ensemble training
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features