mlsanityData to 14 Oct 2026

Hallucination Detection and Mitigation in Large Language Models

arXiv 2601.09929 ↗First posted 14 Jan 2026cs.AIcited by 4

On the map: LLM reasoning → Hallucination detection and mitigation

Abstract (arXiv)

Large Language Models (LLMs) and Large Reasoning Models (LRMs) offer transformative potential for high-stakes domains like finance and law, but their tendency to hallucinate, generating factually incorrect or unsupported content, poses a critical reliability risk. This paper introduces a comprehensive operational framework for hallucination management, built on a continuous improvement cycle driven by root cause awareness. We categorize hallucination sources into model, data, and context-related factors, allowing targeted interventions over generic fixes. The framework integrates multi-faceted detection methods (e.g., uncertainty estimation, reasoning consistency) with stratified mitigation strategies (e.g., knowledge grounding, confidence calibration). We demonstrate its application through a tiered architecture and a financial data extraction case study, where model, context, and data tiers form a closed feedback loop for progressive reliability enhancement. This approach provides a systematic, scalable methodology for building trustworthy generative AI systems in regulated environments.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Hallucination detection and mitigation, one of 17 directions in LLM reasoning. Builds on 29 of its 43 references that are in the map. First posted on arXiv 14 Jan 2026.

Builds on

29 of 43 references.

References · 29

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  4. 2017On Calibration of Modern Neural Networkscited by 4,960
  5. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  6. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  7. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  8. 2019The Curious Case of Neural Text Degenerationcited by 2,916
  9. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  10. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  11. 2022Language Models (Mostly) Know What They Knowcited by 1,856
  12. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
17 more
  1. 2025Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?cited by 982
  2. 2019Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inferencecited by 876
  3. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  4. 2015Weight Uncertainty in Neural Networkscited by 554
  5. 2016Deep Exploration via Bootstrapped DQNcited by 527
  6. 2019Neural Text Generation with Unlikelihood Trainingcited by 503
  7. 2019Deep Ensembles: A Loss Landscape Perspectivecited by 443
  8. 2021Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metricscited by 287
  9. 2022BlenderBot 3: a deployed conversational agent that continually learns to responsibly engagecited by 198
  10. 2024Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Toolscited by 176
  11. 2023Hallucination Augmented Contrastive Learning for Multimodal Large Language Modelcited by 121
  12. 2024A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directionscited by 121
  13. 2023Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucinationcited by 41
  14. 2022Adaptive Temperature Scaling for Robust Calibration of Deep Neural Networkscited by 24
  15. 2022Context-Aware Language Modeling for Goal-Oriented Dialogue Systemscited by 22
  16. 2021Post-hoc loss-calibration for Bayesian neural networkscited by 9
  17. 2025FAITH: A Framework for Assessing Intrinsic Tabular Hallucinations in Financecited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Chance-Constrained Inference for Hallucination Risk Control in Large Language Modelscited by 1
  2. 2026Harnessing Reasoning Trajectories for Hallucination Detection via Answer-agreement Representation Shapingcited by 0
  3. 2026A Unified Approach to Interpreting Knowledge Distillation for Large Language Models via Interactionscited by 0
  4. 2026Hallucination Across the Reasoning Lifecycle: Interface Visibility, Causal Evidence, and Release Control in Large Reasoning Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Theoretical Foundations and Mitigation of Hallucination in Large Language Models
  2. 2024THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models
  3. 2025SelfCheck-Eval: A Multi-Module Framework for Zero-Resource Hallucination Detection in Large Language Models
  4. 2025D2HScore: Reasoning-Aware Hallucination Detection via Semantic Breadth and Depth Analysis in LLMs
  5. 2025From Detection to Diagnosis: Advancing Hallucination Analysis with Automated Data Synthesis
  6. 2025Confidence-Aware Routing for Large Language Model Reliability Enhancement: A Multi-Signal Approach to Pre-Generation Hallucination Mitigation
  7. 2026Scalable Token-Level Hallucination Detection in Large Language Models
  8. 2026BEACON: Behavioral Entropy Aggregation for Cross-Model Hallucination Detection in Large Language Models