Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities
Alexander Nikitin, Jannik Kossen, Yarin Gal, Pekka Marttinen·May 30, 2024
Summary
The paper introduces Kernel Language Entropy (KLE), a novel method for quantifying uncertainty in large language models (LLMs) by focusing on semantic uncertainty. KLE uses positive semidefinite kernels to measure semantic similarities between model outputs and employs von Neumann entropy for fine-grained estimates. It improves upon previous methods like semantic entropy by accounting for pairwise dependencies, leading to better detection of incorrect responses. The approach generalizes existing techniques, showing better performance across diverse datasets and LLM architectures. KLE distinguishes semantic from lexical and syntactic uncertainty, and its variants, KLE and KLE-c, offer different computational trade-offs. The study also evaluates KLE's effectiveness in various tasks, comparing it with other methods and demonstrating its advantages in capturing true semantic uncertainty.
Introduction
Background
Evolution of uncertainty measures in LLMs
Limitations of existing methods (semantic entropy, lexical/syntactic uncertainty)
Objective
Introduce KLE: a novel approach for semantic uncertainty
Highlight key improvements and generalizability
Method
Data Collection
Selection of diverse datasets and LLM architectures
Model outputs and ground truth data collection
Data Preprocessing
Semantic similarity measurement using positive semidefinite kernels
Pairwise dependencies and von Neumann entropy calculation
KLE Calculation
Definition and implementation of KLE
Comparison with semantic entropy and its variants
KLE-c: Computational Trade-offs
Description of KLE-c for efficiency
Analysis of trade-offs between accuracy and computational complexity
Performance Evaluation
Datasets and Architectures
Overview of benchmark datasets
LLM models tested in the study
Comparison with Existing Methods
Quantitative analysis: KLE vs. alternative uncertainty measures
Qualitative evaluation: Case studies and error analysis
Task Applications
Text generation, question answering, and other tasks
Demonstrating KLE's effectiveness in capturing true semantic uncertainty
Conclusion
Summary of KLE's advantages
Implications for LLM research and practical applications
Future directions and potential improvements
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features