Hallucination Detection in Large Language Models Using Diversion Decoding
Quick Answer
The paper presents diversion decoding, a new method for detecting hallucinations in large language models (LLMs) that reduces computational complexity while enhancing uncertainty evaluation.
Quick Take
This approach actively challenges model responses during decoding, yielding better performance than existing probabilistic methods. Experimental results indicate that diversion decoding is a robust solution for improving reliability.
Key Points
- Diversion decoding actively challenges LLM responses during the decoding phase.
- The method significantly reduces computational complexity compared to existing approaches.
- Experimental results show improved performance in detecting hallucinations.
- This technique enhances the reliability and trustworthiness of LLM outputs.
- The paper contributes to ongoing research in LLM uncertainty evaluation.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Large language models (LLMs) have emerged as a powerful tool for retrieving knowledge through seamless, human-like interactions. Despite their advanced text generation capabilities, LLMs exhibit hallucination tendencies, where they generate factually incorrect statements and fabricate knowledge, undermining their reliability and trustworthiness. Multiple studies have explored methods to evaluate LLM uncertainty and detect hallucinations. However, existing approaches are often probabilistic and computationally expensive, limiting their practical applicability. In this paper, we introduce diversion decoding, a novel method for developing an LLM uncertainty heuristic by actively challenging model-generated responses during the decoding phase. Through diversion decoding, we extract features that capture the LLM's resistance to produce alternative answers and utilize these features to train a machine-learning model to develop a heuristic measure of the LLM's uncertainty. Our experimental results demonstrate that diversion decoding outperforms existing methods with significantly lower computational complexity, making it an efficient and robust solution for evaluating hallucination detection.
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.10476 [cs.CL] |
| (or arXiv:2607.10476v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.10476 arXiv-issued DOI via DataCite (pending registration) |
|
| Journal reference: | Data and Applications Security and Privacy XXXIX. DBSec 2025 |
| Related DOI: | https://doi.org/10.1007/978-3-031-96590-6_7
DOI(s) linking to related resources |
Submission history
From: S M Tahmid Siddiqui [view email]
[v1]
Sat, 11 Jul 2026 20:56:42 UTC (1,090 KB)
— Originally published at arxiv.org
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