[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86492-en":3,"doc-seo-86492-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},86492,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Hallucination Detection in Large Language Models Using Diversion Decoding","Large language models (LLMs) are increasingly used to retrieve knowledge through natural, human-like interaction, yet their factual reliability remains constrained by hallucination behavior that invents incorrect claims. Many existing hallucination-detection methods rely on probabilistic estimates and incur high computational costs, limiting deployment. This paper proposes diversion decoding, which actively challenges responses during decoding to measure resistance to alternative answers. Extracted behavioral features train a machine-learning heuristic that evaluates uncertainty. Experiments show improved performance with substantially lower complexity and strong robustness for hallucination detection.","arXiv :2607 . 10476v 1 [ cs .CL] 11 Jul 2026  \nHallucination Detection in Large Language Models using Diversion Decoding  \nBasel Abdeen 1, S M Tahmid Siddiqui 1, Meah Tahmeed Ahmed 1, Anoop Singhal2, Latifur Khan 1, Punya Parag Modi 1 , and Ehab Al-Shaer3  \n1 The University of Texas at Dallas, Richardson, TX 75080, USA {basel.abdeen,tahmid.siddiqui,meah.ahmed,[lkhan}@utdallas.edu](lkhan}@utdallas.edu)[ ](lkhan}@utdallas.edu)2 National Institute of Standards and Technology, Gaithersburg, USA  \n[anoop.singhal@nist.gov](anoop.singhal@nist.gov)  \n3 Carnegie Mellon University, PA, [USA](USA ehab@cmu.edu)[ ehab@cmu.edu](USA ehab@cmu.edu)  \nAbstract. Large language models (LLMs) have emerged as a powerful tool for retrieving knowledge through seamless, human-like interactions.  \nDespite their advanced text generation capabilities, LLMs exhibit hallucination tendencies, where they generate factually incorrect statementsand 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.  \nIn this paper, we introduce diversion decoding, a novel method for developing an LLM uncertainty heuristic by actively challenging modelgenerated 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 machinelearning model to develop a heuristic measure of the LLM’s uncertainty.  \nOur 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.  \nKeywords: large language models · hallucination detection · diversion decoding.  \n1 Introduction  \nRecently, large language models (LLMs) have gained the world’s attention as they started to exhibit a deep understanding of natural language and a solid grasp of the world’s knowledge, prompting people to use them for knowledge retrieval instead of traditional search engines [2,11,21] . Although LLMs have demonstrated high performance in various tasks, their reliability as a source of information is limited due to their tendency to hallucinate [12,14] . Hallucination is LLM behavior that arises when a model is uncertain of its knowledge, leading it to invent facts and generate fake information.  \n2 B. Abdeen et al.  \nMultiple studies have attempted to tackle the LLM hallucination challenge by introducing various scores to develop heuristic measures for LLM uncertainty [6,9,14,18] . These studies proposed approaches that require an external knowledge base, use probabilistic approaches, or exhibit high computational complexity. Unlike these studies, this paper presents a deterministic and computationally efficient approach for quantifying LLMs’ confidence without requiring any external components beyond an existing question-answering dataset. Our approach is inspired by state-of-the-art methodologies that leverage the relationship between consistency and confidence exhibited by LLMs [6,18,23] . When an LLM is confident, it tends to generate the correct answer in various syntactic forms while maintaining semantic similarity. Conversely, when the LLM lacks confidence, it produces semantically different answers for the same question through different decoding paths.  \nUnlike previous studies where multiple answers need to be sampled from an LLM [6,18,23], our approach, diversion decoding, requires generating two distinct answers as follows. We first prompt the LLM with a question and retrieve the greedy answer. Then, we prompt the LLM with the same question; however, whenever the LLM generates an answer that is semantically similar to the first one, we steer its generation toward a different answer. For a sim","cbCaik6N95GmkOPM","https://ap.wps.com/l/cbCaik6N95GmkOPM","pdf",1917517,2,1,18,"English","en",105,"# Introduction\n# Motivation\n# Diversion Decoding Method\n# Problem Statement\n# Evaluation Results\n# Related Work\n# Conclusion","[{\"question\":\"What problem does diversion decoding address in large language models?\",\"answer\":\"It targets the tendency of LLMs to hallucinate by quantifying uncertainty during generation, so incorrect or fabricated answers can be detected more reliably.\"},{\"question\":\"How does diversion decoding create evidence of uncertainty?\",\"answer\":\"It generates a greedy answer for a question, then re-prompts the model and steers decoding away from semantically similar answers, capturing whether the model can resist producing alternatives.\"},{\"question\":\"Why is diversion decoding considered more practical than prior approaches?\",\"answer\":\"It builds a deterministic, computationally efficient uncertainty heuristic without requiring external components beyond an existing question-answering dataset, and experiments show lower complexity than existing 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problem does diversion decoding address in large language models?","Question",{"text":75,"@type":76},"It targets the tendency of LLMs to hallucinate by quantifying uncertainty during generation, so incorrect or fabricated answers can be detected more reliably.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does diversion decoding create evidence of uncertainty?",{"text":80,"@type":76},"It generates a greedy answer for a question, then re-prompts the model and steers decoding away from semantically similar answers, capturing whether the model can resist producing alternatives.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is diversion decoding considered more practical than prior approaches?",{"text":84,"@type":76},"It builds a deterministic, computationally efficient uncertainty heuristic without requiring external components beyond an existing question-answering dataset, and experiments show lower complexity than existing 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