TruthEval: A Dataset to Evaluate LLM Truthfulness and Reliability

2024/06/04 by Aisha Khatun, Daniel G. Brown, Khatun, Aisha +1 · 5 citations
Computer Science · Engineering · Social Sciences · #Artificial Intelligence in Law #Computer science #Engineering #Law, AI, and Intellectual Property #Physics #Reliability (semiconductor) #Reliability engineering

paper · pdf · doi:10.48550/arxiv.2406.01855

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2024/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Large Language Model (LLM) evaluation is currently one of the most important areas of research, with existing benchmarks proving to be insufficient and not completely representative of LLMs' various capabilities. We present a curated collection of challenging statements on sensitive topics for LLM benchmarking called TruthEval. These statements were curated by hand and contain known truth values. The categories were chosen to distinguish LLMs' abilities from their stochastic nature. We perform some initial analyses using this dataset and find several instances of LLMs failing in simple tasks showing their inability to understand simple questions.

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