Estimating Semantic Alphabet Size for LLM Uncertainty Quantification

• URL: http://arxiv.org/abs/2509.14478v1
• Date: Wed, 17 Sep 2025 23:16:39 GMT
• Title: Estimating Semantic Alphabet Size for LLM Uncertainty Quantification
• Authors: Lucas H. McCabe, Rimon Melamed, Thomas Hartvigsen, H. Howie Huang,
• Abstract summary: We propose a modified semantic alphabet size estimator for semantic entropy estimation.<n>Using it to adjust discrete semantic entropy for sample coverage results in more accurate semantic entropy estimation.<n>Our proposed alphabet size estimator flags incorrect LLM responses as well or better than recent top-performing approaches.
• Score: 12.029394705620724
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many black-box techniques for quantifying the uncertainty of large language models (LLMs) rely on repeated LLM sampling, which can be computationally expensive. Therefore, practical applicability demands reliable estimation from few samples. Semantic entropy (SE) is a popular sample-based uncertainty estimator with a discrete formulation attractive for the black-box setting. Recent extensions of semantic entropy exhibit improved LLM hallucination detection, but do so with less interpretable methods that admit additional hyperparameters. For this reason, we revisit the canonical discrete semantic entropy estimator, finding that it underestimates the "true" semantic entropy, as expected from theory. We propose a modified semantic alphabet size estimator, and illustrate that using it to adjust discrete semantic entropy for sample coverage results in more accurate semantic entropy estimation in our setting of interest. Furthermore, our proposed alphabet size estimator flags incorrect LLM responses as well or better than recent top-performing approaches, with the added benefit of remaining highly interpretable.

Related papers

• Efficient semantic uncertainty quantification in language models via diversity-steered sampling [46.23327887393273]
  We introduce a diversity-steered sampler that discourages semantically redundant outputs during decoding.<n>Key idea is to inject a continuous semantic-similarity penalty into the model's proposal distribution.<n>Being modular and requiring no gradient access to the base LLM, the framework promises to serve as a drop-in enhancement for uncertainty estimation.
  arXiv  Detail & Related papers  (2025-10-24T10:06:21Z)
• Semantic Energy: Detecting LLM Hallucination Beyond Entropy [106.92072182161712]
  Large Language Models (LLMs) are being increasingly deployed in real-world applications, but they remain susceptible to hallucinations.<n>Uncertainty estimation is a feasible approach to detect such hallucinations.<n>We introduce Semantic Energy, a novel uncertainty estimation framework.
  arXiv  Detail & Related papers  (2025-08-20T07:33:50Z)
• Beyond Semantic Entropy: Boosting LLM Uncertainty Quantification with Pairwise Semantic Similarity [15.16188621701658]
  Hallucination in large language models can be detected by assessing the uncertainty of model outputs, typically measured using entropy.<n>We propose a simple black-box uncertainty quantification method inspired by nearest neighbor estimates of entropy.<n>Our approach can also be easily extended to white-box settings by incorporating token probabilities.
  arXiv  Detail & Related papers  (2025-05-30T21:21:05Z)
• Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings [11.33157177182775]
  Accurately quantifying uncertainty in large language models (LLMs) is crucial for their reliable deployment. Current state-of-the-art methods for measuring semantic uncertainty in LLMs rely on strict bidirectional entailment criteria. We propose a novel approach that leverages semantic embeddings to achieve smoother and more robust estimation of semantic uncertainty.
  arXiv  Detail & Related papers  (2024-10-30T04:41:46Z)
• Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods [59.779795063072655]
  Chain-of-Thought (CoT) prompting and its variants have gained popularity as effective methods for solving multi-step reasoning problems. We analyze CoT prompting from a statistical estimation perspective, providing a comprehensive characterization of its sample complexity.
  arXiv  Detail & Related papers  (2024-08-25T04:07:18Z)
• Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs [32.901839335074676]
  Hallucinations present a major challenge to the practical adoption of Large Language Models. Recent work by Farquhar et al. (2024) proposes semantic entropy (SE), which can detect hallucinations by estimating uncertainty in the space semantic meaning for a set of model generations. We propose SEPs, which directly approximate SE from the hidden states of a single generation.
  arXiv  Detail & Related papers  (2024-06-22T19:46:06Z)
• REAL Sampling: Boosting Factuality and Diversity of Open-Ended Generation via Asymptotic Entropy [93.8400683020273]
  Decoding methods for large language models (LLMs) usually struggle with the tradeoff between ensuring factuality and maintaining diversity. We propose REAL sampling, a decoding method that improved factuality and diversity over nucleus sampling.
  arXiv  Detail & Related papers  (2024-06-11T21:44:49Z)
• Cycles of Thought: Measuring LLM Confidence through Stable Explanations [53.15438489398938]
  Large language models (LLMs) can reach and even surpass human-level accuracy on a variety of benchmarks, but their overconfidence in incorrect responses is still a well-documented failure mode. We propose a framework for measuring an LLM's uncertainty with respect to the distribution of generated explanations for an answer.
  arXiv  Detail & Related papers  (2024-06-05T16:35:30Z)
• Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities [79.9629927171974]
  Uncertainty in Large Language Models (LLMs) is crucial for applications where safety and reliability are important. We propose Kernel Language Entropy (KLE), a novel method for uncertainty estimation in white- and black-box LLMs.
  arXiv  Detail & Related papers  (2024-05-30T12:42:05Z)
• Amortizing intractable inference in large language models [56.92471123778389]
  We use amortized Bayesian inference to sample from intractable posterior distributions. We empirically demonstrate that this distribution-matching paradigm of LLM fine-tuning can serve as an effective alternative to maximum-likelihood training. As an important application, we interpret chain-of-thought reasoning as a latent variable modeling problem.
  arXiv  Detail & Related papers  (2023-10-06T16:36:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.