How Far Can You Get Without a GPU? A Systematic Benchmark of Lightweight Hallucination Detection Across Question Answering, Dialogue, and Summarisation
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, and a Natural Language Inference (NLI) detector based on a FEVER-trained DeBERTa model, together with a score-level ensemble of similarity and NLI. We ev...
| Authors | Kriti Faujdar, Smit Kadvani |
|---|---|
| Primary category | cs.CL |
| Published | 29 June 2026 |
| Last updated at source | 2 October 2026 |
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