Enabling LLM Knowledge Analysis via Extensive Materialization

• URL: http://arxiv.org/abs/2411.04920v4
• Date: Wed, 04 Jun 2025 10:58:03 GMT
• Title: Enabling LLM Knowledge Analysis via Extensive Materialization
• Abstract summary: We build GPTKB, a knowledge base comprising 101 million triples for over 2.9 million entities GPT-4o-mini.<n>We use GPTKB to internalizedily analyze GPT-4o-mini's factual knowledge in terms of scale, accuracy, bias, and consistency.
• Score: 15.276041443299905
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have majorly advanced NLP and AI, and next to their ability to perform a wide range of procedural tasks, a major success factor is their internalized factual knowledge. Since Petroni et al. (2019), analyzing this knowledge has gained attention. However, most approaches investigate one question at a time via modest-sized pre-defined samples, introducing an ``availability bias'' (Tversky&Kahnemann, 1973) that prevents the analysis of knowledge (or beliefs) of LLMs beyond the experimenter's predisposition. To address this challenge, we propose a novel methodology to comprehensively materialize an LLM's factual knowledge through recursive querying and result consolidation. Our approach is a milestone for LLM research, for the first time providing constructive insights into the scope and structure of LLM knowledge (or beliefs). As a prototype, we build GPTKB, a knowledge base (KB) comprising 101 million relational triples for over 2.9 million entities from GPT-4o-mini. We use GPTKB to exemplarily analyze GPT-4o-mini's factual knowledge in terms of scale, accuracy, bias, cutoff and consistency, at the same time. GPTKB is accessible at https://gptkb.org

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