FLaG models hallucination detection via latent evidence groups and energy-based routing with log-marginal aggregation, claiming SOTA results and a theoretical link to Bayes-optimal detection under heterogeneous mechanisms.
Pham, Hongyu Zhang, and Jun Sun
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FLaG: Fine-Grained Latent Grouping for Hallucination Detection
FLaG models hallucination detection via latent evidence groups and energy-based routing with log-marginal aggregation, claiming SOTA results and a theoretical link to Bayes-optimal detection under heterogeneous mechanisms.