Papers by Alex Robertson

1 papers
KGHaluBench: A Knowledge Graph-Based Hallucination Benchmark for Evaluating the Breadth and Depth of LLM Knowledge (2026.findings-eacl)

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Challenge: Existing benchmarks for large language models are limited by static and narrow questions, leading to limited coverage and misleading evaluations.
Approach: They propose a Knowledge Graph-based hallucination benchmark that assesses Large Language Models across the breadth and depth of their knowledge and provides a fairer and more comprehensive insight into LLM truthfulness.
Outcome: The proposed framework assesses LLMs across breadth and depth of their knowledge, and provides a fairer and more comprehensive insight into LLM truthfulness.

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