arXiv Computation and Language

Single-Pass Uncertainty Heads for Claim-Level Hallucination Detection in Persian Medical Language Models

The paper introduces a single‑pass uncertainty head for detecting hallucinations in Persian medical language models. By adapting the LLM Uncertainty Head framework to Aya‑Expanse‑8B and two Gaokerena backbones, the authors build claim‑level hallucination datasets and train lightweight heads that achieve PR‑AUCs of 0.4820 and 0.4652 and ROC‑AUCs of 0.7852 and 0.7810. These heads operate without retrieval or repeated sampling during inference, offering a cost‑effective approach to hallucination detection in Persian medical LLMs.

arXiv Computation and Language

Sep 4

Gaokerena: A Small Persian Medical Language Model Family

Gaokerena is a family of compact Persian medical language models designed for consumer‑grade hardware. The first variant, Gaokerena‑V, was trained on a 90‑million‑token Persian medical corpus and 20,000 physician Q&A pairs, raising a translated medical MMLU benchmark score from 46.28% to 49.31%. A second variant, Gaokerena‑R, adds a Chain‑of‑Thought approach and two Reinforcement Learning with AI Feedback frameworks, achieving a higher benchmark score of 52.98% while also providing uncertainty estimates based on internal hidden states.

By Mehrdad Ghassabi, Hamidreza Baradaran Kashani, Pedram Rostami, Sadra Hakim, Zahra Kazemi, Amirhossein Poursina, Milad Tavakoli, Audrina Ebrahimi

llms reinforcement-learning benchmarks safety

arXiv Computation and Language

Sep 23

Geometric Uncertainty for Detecting and Correcting Hallucinations in LLMs

The paper presents a geometric framework for quantifying uncertainty in large language models (LLMs) at both the prompt and answer levels. By modeling a prompt-conditioned semantic distribution in answer embedding space and using archetypal analysis on multiple sampled answers, the method estimates distribution entropy for prompt-level uncertainty and atypicality for individual answer reliability. Experiments demonstrate comparable or superior performance to existing techniques on short-form QA datasets and notably better results on medical datasets where hallucinations pose critical risks.

By Edward Phillips, Sean Wu, Soheila Molaei, Danielle Belgrave, Anshul Thakur, David Clifton

llms rag safety

arXiv Computation and Language

Oct 1

Lowest Span Confidence: Zero-Shot Hallucination Detection from a Single LLM Response

arXiv:2601.19918v2 Announce Type: replace Abstract: Hallucinations in Large Language Models (LLMs), i.e., plausible but non-factual generations, pose a significant challenge to reliable deployment in...

By Yitong Qiao, Licheng Pan, Yu Mi, Lei Liu, Yue Shen, Jian Wang, Jinjie Gu, Fei Sun, Zhixuan Chu

llms benchmarks safety

arXiv AI

Aug 11

When Confidence Fails: Overconfidence in LLMs under Uncertainty and Missing Clinical Information

arXiv:2608. 09080v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance in medical question answering and clinical reasoning tasks.

By Maryam Tahermazandarani, Adnan Mahmood, Fahmida Islam, Quan Z. Sheng

llms nlp

arXiv AI

Aug 18

Toward Better Assessment of LLMs' Performance in Clinical Error Detection

arXiv:2608. 16643v1 Announce Type: cross Abstract: Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation.

By Yifan Zhang, Rahmatollah Beheshti

llms nlp benchmarks safety

arXiv Computation and Language

Aug 25

Claim-Level Confidence Calibration for Reliable Decision Making with Large Language Models

arXiv:2608.22483v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly support decision-making in high-stakes domains, but they often hallucinate and express confidence that is mis...

By Toghrul Abbasli, Kentaroh Toyoda, Yuan Wang, Li Chen

llms fine-tuning safety

arXiv AI

Aug 26

Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Review

arXiv:2504.18346v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have been transformative across many domains. However, hallucination, i.e., confidently outputting incorrect inf...

By Toghrul Abbasli, Kentaroh Toyoda, Yuan Wang, Leon Witt, Muhammad Asif Ali, Yukai Miao, Dan Li, Qingsong Wei

llms benchmarks safety

arXiv AI

Jul 23

Rethinking Uncertainty Evaluation in Large Language Models

arXiv:2607. 19367v1 Announce Type: new Abstract: Calibration is the primary criterion for evaluating LLM confidence, but it is insufficient: it admits trivially incoherent estimators, depends on the evaluation distribution, and does not test the extent to which the estimation can be interpreted as a consistent, underlying probability function.

By Krish Matta, Atharv Naphade, Andy Zou

llms reinforcement-learning

arXiv AI

Sep 30

The Detectability Gap: Hidden Heterogeneity in Hallucination Detection Across Language Models

arXiv:2609.35860v1 Announce Type: cross Abstract: Sampling based consistency is widely used for hallucination detection, yet aggregate performance can conceal systematic differences in which errors a...

By Pranav Darshan, Pranav A, Sravan Karthick T, Minal Moharir, Ivan P. Yamshchikov

llms diffusion nlp safety

arXiv AI

Sep 15

Route, Don't Fix: Regime-Dependent Decoding Correction and a Trajectory-Gated Router for Reliable Clinical LLM Answer Selection

arXiv:2609.14825v1 Announce Type: cross Abstract: Large language models (LLMs) are often deemed unsafe for clinical question answering because of their tendency to hallucinate. Retrieval augmentation...

By Zeyu Dong, Benjamin Wang, Joyee W. Jin

llms nlp fine-tuning benchmarks

arXiv AI

Sep 1

Small Updates, Big Doubts: Does Parameter-Efficient Fine-tuning Enhance Hallucination Detection ?

arXiv:2602.11166v2 Announce Type: replace-cross Abstract: Parameter-efficient fine-tuning (PEFT) methods are widely used to adapt large language models (LLMs) to downstream tasks and are often assume...

By Xu Hu, Yifan Zhang, Songtao Wei, Chen Zhao, Qiannan Li, Bingzhe Li, Feng Chen

llms fine-tuning benchmarks safety

arXiv AI

Jun 19

LLM Doesn't Know What It Doesn't Know: Detecting Epistemic Blind Spots via Cross-Model Attribution Divergence on Clinical Tabular Data

arXiv:2606. 19509v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly applied to structured clinical data, yet whether they can recognize the limits of their own knowledge on such tasks remains unexplored.

By Akshat Dasula, Prasanna Desikan, Jaideep Srivastava

llms

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