Large Language Models (LLMs) possess a remarkable capacity to generate persuasive and intelligible language. However, coherence does not equate to truthfulness, as the responses often contain subtle hallucinations. Existing benchmarks are limited by static and narrow questions, leading to limited coverage and misleading evaluations. We present **KGHaluBench**, a Knowledge Graph-based hallucination benchmark that assesses LLMs across the breadth and depth of their knowledge, providing a fairer and more comprehensive insight into LLM truthfulness. Our framework utilises the KG to dynamically construct challenging, multifaceted questions, whose difficulty is then statistically estimated to address popularity bias. Our automated verification pipeline detects abstentions and verifies the LLM’s response at both conceptual and correctness levels to identify different types of hallucinations. We evaluate 25 frontier models, using novel accuracy and hallucination metrics. The results provide a more interpretable insight into the knowledge factors that cause hallucinations across different model sizes. KGHaluBench is publicly available to support future developments in hallucination mitigation.

In this paper, we propose a sieve filtering-based approach that can retrieve facts to invalidate claims made in social media posts. The fact filters are initially coarse-grained, based on the original language of the social media posts, and end with fine-grained filters based on the exact time frame in which the posts were uploaded online. This streamlined approach achieved a 0.883 retrieval success rate in the monolingual task while maintaining a scalable efficiency level of processing a social media post per 0.07 seconds.