Decoding Intelligence: A Framework for Certifying Knowledge Comprehension in LLMs

2024-02-24

AI Summary

Existing evaluations of large language models’ (LLMs) knowledge understanding rely on small-scale, ad hoc test sets, lacking generalizability and statistical reliability guarantees. Method: We propose the first formal certification framework for knowledge understanding with rigorous probabilistic guarantees. Leveraging Wikidata5M as a semantic reference, the framework integrates probabilistic modeling and statistical inference to derive tight, high-confidence upper bounds on the probability of incorrect responses—certifying correctness for arbitrary knowledge prompts. Contribution/Results: The framework is provably sound and generalizable, enabling the first strict, formal verification of LLMs’ knowledge understanding capability. Extensive experiments across multiple state-of-the-art models demonstrate its effectiveness: certificates hold for in-distribution prompts, and empirical results confirm that scaling model size significantly improves knowledge understanding reliability—quantified via our certified bounds.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Knowledge comprehension capability is an important aspect of human intelligence. As Large Language Models (LLMs) are being envisioned as superhuman agents, it is crucial for them to be proficient at knowledge comprehension. However, existing benchmarking studies do not provide consistent, generalizable, and formal guarantees on the knowledge comprehension capabilities of LLMs. In this work, we propose the first framework to certify knowledge comprehension in LLMs with formal probabilistic guarantees. Our certificates are quantitative -- they consist of high-confidence, tight bounds on the probability that a target LLM gives the correct answer on any knowledge comprehension prompt sampled from a distribution. We design and certify novel specifications that precisely represent distributions of knowledge comprehension prompts leveraging knowledge graphs. We certify SOTA LLMs for specifications over the Wikidata5m knowledge graph. We find that the knowledge comprehension capability improves significantly with scaling the size of the models.

Problem

• Certifying LLM knowledge comprehension reliability formally
• Addressing limited test set generalizability concerns
• Identifying vulnerabilities from natural noise in prompts

Innovation

• Formal probabilistic guarantees for reliability
• Specifications using knowledge graphs
• Quantitative certificates for high-confidence bounds