Certifying Knowledge Comprehension in LLMs

University of Illinois Urbana-Champaign

Abstract

Large Language Models (LLMs) are increasingly used to answer questions based on their learned knowledge. However, existing evaluation methods are limited by small test sets and do not provide formal guarantees. We introduce the first specification and certification framework for knowledge comprehension in LLMs, providing formal probabilistic guarantees for reliability. Our novel specifications use knowledge graphs to represent large distributions of knowledge comprehension prompts. Applying our framework to precision medicine and general question-answering, we demonstrate vulnerabilities in state-of-the-art LLMs due to natural noise in prompts. Our certification framework establishes performance hierarchies among LLMs, providing quantitative certificates with high-confidence bounds on performance. This approach significantly advances rigorous assessment of LLMs' knowledge comprehension capabilities.

Key Innovations

First Formal Certification Framework: Our work introduces the first mathematical framework for certifying knowledge comprehension in LLMs with formal guarantees.
Knowledge Graph Specifications: Novel use of knowledge graphs to represent large distributions of comprehension prompts.
Quantitative Certificates: Provides high-confidence bounds on LLM performance across different prompt distributions.
Vulnerability Detection: Reveals how natural prompt variations can significantly degrade LLM performance.
Domain Adaptability: Demonstrated in both precision medicine and general question-answering domains.

Overview

Large Language Models (LLMs) have demonstrated remarkable abilities in answering knowledge-based questions, making them valuable tools for domains like precision medicine. However, ensuring their reliability requires rigorous assessment methods beyond traditional evaluations that are limited by small test sets and lack formal guarantees.

Our framework addresses this gap by introducing a formal specification and certification approach for knowledge comprehension in LLMs. We represent knowledge as structured graphs, enabling us to generate quantitative certificates that provide high-confidence bounds on LLM performance across large prompt distributions.

Knowledge Certification Framework — Framework Overview: Our certification framework evaluates an LLM's knowledge comprehension by (1) using knowledge graphs to represent prompt distributions, (2) generating diverse prompts with natural variations, (3) evaluating responses against ground truth, and (4) producing formal certificates with probabilistic guarantees.

Through applying our framework to precision medicine and general question-answering domains, we demonstrate how naturally occurring noise in prompts can affect response accuracy in state-of-the-art LLMs. We establish performance hierarchies among SOTA LLMs and provide quantitative metrics that can guide their future development and deployment in knowledge-critical applications.

Our certification methodology bridges the gap between theoretical rigor and practical evaluation, offering a robust approach to assessing and certifying LLMs for knowledge-intensive tasks.

Methodology

Our certification framework consists of several key components:

Knowledge Graph Representation

We use knowledge graphs to mathematically represent large distributions of prompts, enabling comprehensive testing across diverse knowledge domains. This approach allows us to capture relationships between concepts and generate diverse question formulations.

Prompt Distribution Generation

From these knowledge graphs, we systematically generate distributions of prompts that test an LLM's knowledge comprehension capabilities. These distributions include natural variations in phrasing, complexity, and specificity.

Quantitative Certification

We provide formal probabilistic guarantees through quantitative certificates that bound the probability of an LLM giving incorrect answers when faced with prompts from the specified distribution.

Key Results

Uncovering Vulnerabilities

Our certification framework demonstrates vulnerabilities in state-of-the-art LLMs due to naturally occurring noise in prompts which can be formalized through our framework, showing how even minor variations in phrasing and structure can significantly impact response reliability.

Performance Hierarchy

We establish a clear performance hierarchy among modern LLMs in knowledge comprehension tasks, providing quantitative metrics for comparison.

Domain-Specific Insights

In precision medicine, we show how certification can identify which models are most reliable for answering critical healthcare questions, with implications for clinical deployment.

Example of Distracted LLM Responses — Example of LLM performance degradation due to natural prompt variations. Our framework can certify reliability bounds even in the presence of such distractions in input prompts.

@article{chaudhary2024certifying, title={Certifying Knowledge Comprehension in LLMs}, author={Chaudhary, Isha and Jain, Vedaant V. and Singh, Gagandeep}, journal={arXiv preprint arXiv:2402.15929}, year={2024} }

Ethics Statement

Our work aims to enhance the reliability of LLMs in knowledge-critical domains like healthcare through rigorous certification. While our framework helps identify vulnerabilities, it also provides a path toward more trustworthy AI systems. We recognize the potential societal impacts of LLM deployment in sensitive domains and believe our certification approach contributes to responsible AI development by providing formal guarantees about model reliability. We have made our framework open-source to promote transparency and facilitate broader community engagement in improving LLM reliability.