Head-to-Tail: How Knowledgeable are Large Language Models ( LLM s)? A . K . A . Will LLM s Replace Knowledge Graphs? (2024)

Abstract: 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.

## introduction pre-trained large language models (llms), such as chatgpt foot_0 , @xcite , and llama 2 @xcite , have demonstrated impressive capabilities in internalizing knowledge and responding to common inquiries @xcite . nevertheless, these models often lack knowledge of nuanced, domain-specific details and are susceptible to hallucinations @xcite , underscoring the significant challenges of increasing the factuality of llms and minimizing hallucinations from llm responses. conversely, the rise of llms has sparked debates on whether knowledge graphs (kgs), which store real-world factual knowledge in triplet form (subject, predicate, object), will be replaced with llms. this paper tries to answer these questions from a new angle: how knowledgeable are llms? ## conclusion we introduce head-to-tail, the first benchmark designed to assess the ability of llms to internalize head, torso, and tail facts. alongside the dataset, we present a new evaluation methodology with appropriate metrics for automatically evaluating llms' factuality. our evaluation shows that even the most advanced llms have notable limitations in representing factual knowledge, particularly for the torso and tail entities. accordingly, we suggest new research areas to seamlessly blend knowledge in the symbolic form and neural form.