Public articles linked to the same research event.
arXiv The study adapts the LLM Uncertainty Head framework to two Persian medical models built on Aya-Expanse-8B, Gaokerena-V and Gaokerena-R; it first observes on a 168-question Iranian medical entrance examination that Gaokerena-V has substantially lower five-run consistency than Aya-Expanse-8B while Gaokerena-R is comparable, then builds Persian claim-level hallucination datasets with 1,600 responses per backbone and trains lightweight claim-level heads on frozen backbone attention maps and token probabilities, obtaining held-out PR-AUCs of 0.4820 and 0.4652 (2.30 and 2.66 times their random baselines) and ROC-AUCs of 0.7852 and 0.7810, with no retrieval or repeated sampling at inference.
The study adapts the LLM Uncertainty Head framework to two Persian medical models built on Aya-Expanse-8B, Gaokerena-V and Gaokerena-R; it first observes on a 168-question Iranian medical entrance examination that Gaokerena-V has substantially lower five-run consistency than Aya-Expanse-8B while Gaokerena-R is comparable, then builds Persian claim-level hallucination datasets with 1,600 responses per backbone and trains lightweight claim-level heads on frozen backbone attention maps and token probabilities, obtaining held-out PR-AUCs of 0.4820 and 0.4652 (2.30 and 2.66 times their random baselines) and ROC-AUCs of 0.7852 and 0.7810, with no retrieval or repeated sampling at inference.
The study adapts the LLM Uncertainty Head framework to two Persian medical models built on Aya-Expanse-8B, Gaokerena-V and Gaokerena-R; it first observes on a 168-question Iranian medical entrance examination that Gaokerena-V has substantially lower five-run consistency than Aya-Expanse-8B while Gaokerena-R is comparable, then builds Persian claim-level hallucination datasets with 1,600 responses per backbone and trains lightweight claim-level heads on frozen backbone attention maps and token probabilities, obtaining held-out PR-AUCs of 0.4820 and 0.4652 (2.30 and 2.66 times their random baselines) and ROC-AUCs of 0.7852 and 0.7810, with no retrieval or repeated sampling at inference.
The study adapts the LLM Uncertainty Head framework to two Persian medical models built on Aya-Expanse-8B, Gaokerena-V and Gaokerena-R; it first observes on a 168-question Iranian medical entrance examination that Gaokerena-V has substantially lower five-run consistency than Aya-Expanse-8B while Gaokerena-R is comparable, then builds Persian claim-level hallucination datasets with 1,600 responses per backbone and trains lightweight claim-level heads on frozen backbone attention maps and token probabilities, obtaining held-out PR-AUCs of 0.4820 and 0.4652 (2.30 and 2.66 times their random baselines) and ROC-AUCs of 0.7852 and 0.7810, with no retrieval or repeated sampling at inference.