Large language models (LLMs) are susceptible to hallucinations -- factually incorrect outputs -- leading to a large body of work on detecting and mitigating such cases. We argue that it is important to distinguish between two types of hallucinations: ones where the model does not hold the correct answer in its parameters, which we term HK-, and ones where the model answers incorrectly despite having the required knowledge, termed HK+. We first find that HK+ hallucinations are prevalent and occur across models and datasets. Then, we demonstrate that distinguishing between these two cases is beneficial for mitigating hallucinations. Importantly, we show that different models hallucinate on different examples, which motivates constructing model-specific hallucination datasets for training detectors. Overall, our findings draw attention to classifying types of hallucinations and provide means to handle them more effectively. The code is available at https://github.com/technion-cs-nlp/hallucination-mitigation .
Hallucination as Commitment Failure: Larger LLMs Misfire Despite Knowing the Answer
Graduate School of Data Science（数据科学研究生院）；Department of Rural Systems Engineering（农村系统工程系）；Electrical Engineering and Computer Science（电子工程与计算机科学）；Department of Aerospace Engineering（航空航天工程系）
"I know myself better, but not really greatly": How Well Can LLMs Detect and Explain LLM-Generated Texts?
School of Cyber Science and Engineering, Shanghai Jiao Tong University, China（上海交通大学信息科学与工程学院）；Department of Computing Science, University of Aberdeen, UK（阿伯丁大学计算机科学系）；Institute of Cyber Security for Society (iCSS) & School of Computing, University of Kent, UK（社会网络安全研究所（iCSS）及肯特大学计算机学院）
Detection and Mitigation of Hallucination in Large Reasoning Models: A Mechanistic Perspective
Gaoling School of Artificial Intelligence（北京理工大学人工智能学院）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation（下一代智能搜索与推荐工程研究中心）
