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

Hallucination detection is possible without expensive resources: The study demonstrates that CPU-feasible methods using public models can effectively detect when AI systems generate false information, achieving F1 scores of 0.792 on question answering and 0.694 on dialogue tasks—making trustworthy AI deployment more accessible to resource-constrained organizations and researchers.

Performance varies significantly by task type: The same detection methods work well for question answering and dialogue but perform near chance level on summarization tasks (AUC-ROC between 0.469-0.574), suggesting that organizations need task-specific approaches rather than one-size-fits-all hallucination detection solutions.

Combining multiple detection methods improves reliability: A ensemble approach combining semantic similarity and Natural Language Inference (NLI) detection proved most consistent across tasks, indicating that responsible AI practitioners should use multiple complementary detection strategies rather than relying on single methods.

Detection design choices matter as much as the underlying method: The researchers found that summarization failures were "largely" due to their single-pass detection design rather than fundamental limitations, suggesting that how hallucination detection systems are implemented—not just which models are used—significantly impacts their effectiveness.

Accessible hallucination detection removes barriers to responsible deployment: By showing that effective detection doesn't require GPUs or proprietary APIs, this work helps democratize access to tools needed for trustworthy AI, enabling smaller organizations to implement safeguards previously available only to well-resourced companies.