The paper presents an empirical taxonomy of eleven hallucination sub‑types in large language models (LLMs) that generate UML class diagrams from natural language requirements, organized into Prompt Faithfulness, Code Faithfulness, and Reasoning Quality. The taxonomy was derived from 60 manually annotated DeepSeek‑R1 traces and applied to additional traces, revealing model‑dependent failure patterns and a negative association between requirement complexity and Prompt Faithfulness errors. A mitigation experiment with Qwen3‑based models showed that adding Prompt Faithfulness and Reasoning Quality constraints to the prompt improved the F1 score by 4.0 percentage points, while post‑hoc Code Faithfulness review offered no consistent benefit.
By Mojdeh Rahmanian, Foutse Khomh, Ashkan Sami, Yanchao Yu
The paper proposes a method for combining expert knowledge using mixtures of feedback fuzzy cognitive maps (FCMs) guided by a relevance criterion adapted from legal evidence theory. An answer is deemed relevant only if it is both probative and material to the query, and this relevance is used to weight and mix expert FCMs into a query‑specific knowledge network. The authors introduce a Bayes‑like de‑mixing theorem to estimate posterior‑like FCMs and demonstrate the approach on synthetic FCMs derived from a 13‑node expert model of ischemic‑stroke risk.
By Olaoluwa Adigun, Akash Kumar Panda, Bart Kosko
The paper presents a framework and results of a multi-dimensional analysis of GPTKB v1.5, a 100‑million‑fact knowledge base elicited from GPT‑4.1. It shows that the LLM’s factual knowledge differs markedly from established knowledge bases and that its accuracy is lower than suggested by prior benchmarks. The study also identifies inconsistency, ambiguity, and hallucinations as major issues, pointing to future research directions in neuro‑symbolic AI for extracting, consolidating, and verifying factual LLM knowledge.
By Shrestha Ghosh, Luca Giordano, Yujia Hu, Tuan-Phong Nguyen, Simon Razniewski
The paper introduces GRACE, a framework that breaks down large language model (LLM) responses into atomic claims and grounds them against trusted knowledge priors using a weighted bipartite graph. Edge weights enable weighted centrality analysis to classify claims as Grounded, Refuted, or Boundary, identifying hallucinations and frontier knowledge. An objective called Return on Attention (RoA) prioritizes expert review only for high‑uncertainty claims, and verified claims become new evidence anchors, creating a loop that expands the knowledge base across iterations.
By John Seon Keun Yi, Joshua R. Minot, Dokyun Lee
The paper explores using a language model’s low‑level symbolic skill—specifically SQL—to detect hallucinations without fine‑tuning. By having the model construct an SQL database from reference documents, it can reason over both the source and the model’s output, creating a neurosymbolic check. Experiments on RAGTruth and DiaHalu show this method outperforms direct prediction and rivals state‑of‑the‑art detectors, highlighting the value of leveraging inherent symbolic competences in LLMs.
By Renato Vukovic, Hsien-chin Lin, Carel van Niekerk, Benjamin Ruppik, Michael Heck, Shutong Feng, Nurul Lubis, Milica Gasic
arXiv:2601.10485v5 Announce Type: replace
Abstract: Domain-specific knowledge graphs (DKGs) are critical yet often suffer from limited coverage compared to General Knowledge Graphs (GKGs). Existing t...
By Runhao Zhao, Weixin Zeng, Wentao Zhang, Chong Chen, Zhengpin Li, Xiang Zhao, Lei Chen