arXiv:2507. 06722v2 Announce Type: replace-cross Abstract: Understanding how large language models (LLMs) internally represent and process their predictions is central to detecting uncertainty and preventing hallucinations.
arXiv:2603. 24929v2 Announce Type: replace Abstract: Understanding and quantifying uncertainty in large language model (LLM) outputs is critical for reliable deployment.
arXiv:2606. 19353v1 Announce Type: cross Abstract: In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model's ability to understand the context, obscuring whether failures arise from data properties or model limitations.
arXiv:2603. 24967v2 Announce Type: replace Abstract: Understanding why a large language model (LLM) is uncertain about the response is important for their reliable deployment.
arXiv:2605. 22864v2 Announce Type: replace Abstract: The maximum softmax probability (MSP) represents a default approach when evaluating uncertainty quantification for language model generation with structured output.
The paper introduces Prediction of Prediction (PoP), a method that fuses intermediate hidden representations across transformer layers during a single forward pass to detect hallucinations in large language models. PoP leverages internal hidden‑state transition dynamics to signal factual errors without extra decoding steps, achieving a 75.5% AUROC on the TruthfulQA benchmark with less than 1.2% added latency.
DynHD is a method for detecting hallucinations in diffusion large language models (D‑LLMs) by focusing on token‑level uncertainty and its evolution during the denoising process. It introduces a semantic‑aware evidence construction module that filters out non‑informative structural tokens and highlights uncertainty in informative tokens, and a reference evidence generator that models the expected trajectory of uncertainty, enabling a deviation‑based detector to identify hallucinations. Experiments show DynHD outperforms existing baselines while being more efficient across various benchmarks and backbone models.
arXiv:2609.38962v1 Announce Type: new Abstract: Recent work on hallucination detection in large language models has shown that, for a fixed pre-trained model and reasoning task, it is possible to est...
The paper investigates the relationship between a large language model’s internal probability distribution and its verbalized confidence statements. By systematically manipulating training and in‑context data, the authors show that both internal and verbalized probabilities are influenced by distributional and asserted uncertainty in the data. They find that verbalized probabilities align with internal ones beyond what would be expected if they tracked the same sources independently, indicating that verbalized confidence can serve as a probe of the model’s internal distribution.
arXiv:2606. 27679v1 Announce Type: cross Abstract: Probe-based uncertainty estimation (UE) has emerged as a prominent approach to detect hallucinations in Large Language Models (LLMs) by learning uncertainty from internal model signals.
arXiv:2608. 03817v1 Announce Type: cross Abstract: Large vision--language models (LVLMs) demonstrate strong multimodal reasoning capabilities but remain prone to hallucination, where model predictions are not grounded in visual evidence.
Recent advancements in Large Language Models (LLMs) have enabled sophisticated reasoning and content generation, yet their inherent stochasticity poses significant challenges for ensuring predictive credibility. While traditional uncertainty taxonomy paradigms, such as the dichotomy of aleatoric and epistemic uncertainties, provide conceptual foundations, they often fail to capture the multi-component and multi-stage nature of LLM generation and struggle to evaluate the effectiveness of various Uncertainty Quantification (UQ) methods.
The paper challenges the common practice of estimating aleatoric uncertainty in large language models (LLMs) by generating multiple clarified inputs and comparing the resulting answers. It argues that answers are unnecessary, costly, and can introduce epistemic leakage, proposing instead a clarification-only method that directly assesses ambiguity from the space of plausible interpretations. Experiments on three benchmarks show the new approach improves AUROC, reduces computational cost, and yields uncertainty estimates less correlated with epistemic uncertainty.