Token Level Hallucination Detection via Variance in Language Models

• URL: http://arxiv.org/abs/2507.04137v1
• Date: Sat, 05 Jul 2025 19:20:59 GMT
• Title: Token Level Hallucination Detection via Variance in Language Models
• Authors: Keshav Kumar,
• Abstract summary: Large Language Models (LLMs) have demonstrated impressive generative capabilities across diverse tasks but remain susceptible to hallucinations.<n>We introduce a reference-free, token-level hallucination detection framework that leverages the variance in token log-probabilities across multiple generations.<n>Our approach is model-agnostic, interpretable, and suited for real-time or post-hoc analysis.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated impressive generative capabilities across diverse tasks but remain susceptible to hallucinations, confidently generated yet factually incorrect outputs. We introduce a reference-free, token-level hallucination detection framework that leverages the variance in token log-probabilities across multiple stochastic generations. Unlike prior methods that require ground-truth references or sentence-level verification, our approach is model-agnostic, interpretable, and suited for real-time or post-hoc analysis. We evaluate our method on unanswerable question prompts from the SQuAD v2 dataset and benchmark across three autoregressive models of varying scales: GPT-Neo 125M, Falcon 1B, and Mistral 7B. Through both quantitative metrics and visual diagnostics, we show that token-level variance reliably highlights instability in model outputs and correlates with hallucination patterns. Our framework is lightweight, reproducible, and adaptable to multiple domains, offering a valuable diagnostic tool for analyzing generative reliability in LLMs.

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