How Far Can You Get Without a GPU? A Systematic Benchmark of Lightweight Hallucination Detection Across Question Answering, Dialogue, and Summarisation

arXiv:2606.29809v2 Announce Type: replace-cross Abstract: Hallucination detection has become a pressing requirement for trustworthy AI deployment at scale. The most accurate detection methods depend on GPU-intensive inference, proprietary API calls, or white-box access to the generating model, putting them out of reach for resource-constrained researchers and practitioners. We explore a practical alternative: how well can hallucination detection perform using only lightweight, CPU-feasible methods built on public models? We benchmark four such detectors, ROUGE-L, semantic similarity, BERTScore

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