arXiv:2607. 08377v1 Announce Type: new Abstract: Uncertainty quantification is central to the reliable deployment of large language models (LLMs), and eigenvalues of semantic embeddings have recently emerged as a key tool in state-of-the-art methods.
Uncertainty quantification is central to the reliable deployment of large language models (LLMs), and eigenvalues of semantic embeddings have recently emerged as a key tool in state-of-the-art methods. However, conventional calibration results developed for classification probabilities cannot be directly transferred to eigenvalues.
arXiv:2606. 32012v1 Announce Type: new Abstract: Uncertainty estimation has been a long-standing challenge in AI models; it amounts to "knowing what you don't know," and metacognition is notoriously difficult even for humans (cf.
arXiv:2608. 09011v1 Announce Type: new Abstract: Uncertainty Quantification (UQ) aims to measure the reliability of model predictions, serving as a critical safeguard for deploying Vision-Language Models (VLMs) in safety-critical scenarios.
Semantic Self-Distillation (SSD) is a method that distills the semantic dispersion of sampled answers from large language models into lightweight student models. These students estimate a prompt-conditioned density before answer generation, providing a prompt-level uncertainty signal via entropy and an answer-level reliability measure through probability density. Experiments on TriviaQA and MMLU show that SSD matches the teacher’s uncertainty estimates while enabling additional tasks such as hallucination prediction, out-of-domain detection, and multiple-choice answer selection.
arXiv:2606. 02093v1 Announce Type: cross Abstract: The task of Error Prediction, namely predicting whether a model output is correct, is commonly tackled with Uncertainty Quantification (UQ).
arXiv:2605.27136v2 Announce Type: replace Abstract: Uncertainty quantification (UQ) remains a critical challenge in Large Vision Language Models (LVLMs) for reliable predictions and real-world deploy...
arXiv:2606. 31407v1 Announce Type: cross Abstract: Vision-language models can produce confident answers on visually ambiguous inputs, resulting in biased predictions.
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.
CUSP (Collective Uncertainty through Semantic Opinion Pooling) is a training‑free framework that aggregates responses from multiple vision‑language models into a shared semantic space, producing a pooled opinion and two system‑level uncertainty signals: collective uncertainty (dispersion) and Jensen‑Shannon divergence (model conflict). It decomposes collective entropy into the mean of individual semantic entropies plus JSD, enabling reliable uncertainty estimation without token logits or calibration labels. In static ensembles, collective uncertainty outperforms baseline methods for error detection and abstention, while JSD excels in commercial settings, and the pooled prediction consistently improves accuracy over individual models. "whyItMatters":"CUSP provides a practical, model‑agnostic way to quantify system‑level reliability and improve decision‑making in multimodal reasoning tasks."
arXiv:2603. 29466v2 Announce Type: replace-cross Abstract: Existing methods for quantifying predictive uncertainty in neural networks are either computationally intractable for large language models or require access to training data that is typically unavailable.
arXiv:2605. 04638v2 Announce Type: replace-cross Abstract: Uncertainty quantification (UQ) is an important technique for ensuring the trustworthiness of LLMs, given their tendency to hallucinate.