Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?
This addresses the question of whether LLMs can replace knowledge graphs by evaluating their factual knowledge, though it is incremental as it builds on existing benchmarking efforts.
The paper tackled the problem of assessing how knowledgeable large language models (LLMs) are by constructing Head-to-Tail, a benchmark with 18K question-answer pairs across head, torso, and tail facts, and found that existing LLMs are far from perfect in factual knowledge, particularly for torso-to-tail entities.
Since the recent prosperity of Large Language Models (LLMs), there have been interleaved discussions regarding how to reduce hallucinations from LLM responses, how to increase the factuality of LLMs, and whether Knowledge Graphs (KGs), which store the world knowledge in a symbolic form, will be replaced with LLMs. In this paper, we try to answer these questions from a new angle: How knowledgeable are LLMs? To answer this question, we constructed Head-to-Tail, a benchmark that consists of 18K question-answer (QA) pairs regarding head, torso, and tail facts in terms of popularity. We designed an automated evaluation method and a set of metrics that closely approximate the knowledge an LLM confidently internalizes. Through a comprehensive evaluation of 16 publicly available LLMs, we show that existing LLMs are still far from being perfect in terms of their grasp of factual knowledge, especially for facts of torso-to-tail entities.