Enhancing Critical Thinking with AI: A Tailored Warning System for RAG Models
On the map: LLM reasoning → Hallucination detection and mitigation
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Abstract (arXiv)
Retrieval-Augmented Generation (RAG) systems offer a powerful approach to enhancing large language model (LLM) outputs by incorporating fact-checked, contextually relevant information. However, fairness and reliability concerns persist, as hallucinations can emerge at both the retrieval and generation stages, affecting users' reasoning and decision-making. Our research explores how tailored warning messages -- whose content depends on the specific context of hallucination -- shape user reasoning and actions in an educational quiz setting. Preliminary findings suggest that while warnings improve accuracy and awareness of high-level hallucinations, they may also introduce cognitive friction, leading to confusion and diminished trust in the system. By examining these interactions, this work contributes to the broader goal of AI-augmented reasoning: developing systems that actively support human reflection, critical thinking, and informed decision-making rather than passive information consumption.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Hallucination detection and mitigation, one of 17 directions in LLM reasoning. Builds on 8 of its 17 references that are in the map. First posted on arXiv 23 Apr 2025.
Builds on
8 of 17 references.
References · 8
- 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Eracited by 98
- 2024Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perceptioncited by 56
- 2024Decoding Biases: Automated Methods and LLM Judges for Gender Bias Detection in Language Modelscited by 33
- 2024PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agentscited by 30
- 2024RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Modelscited by 27
- 2024Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systemscited by 26
- 2024Retrieval Meets Reasoning: Even High-school Textbook Knowledge Benefits Multimodal Reasoningcited by 14
- 2024Fakes of Varying Shades: How Warning Affects Human Perception and Engagement Regarding LLM Hallucinationscited by 12
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- 2024RAGged Edges: The Double-Edged Sword of Retrieval-Augmented Chatbots
- 2026PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations
- 2025Mitigating Hallucination in Large Language Models (LLMs): An Application-Oriented Survey on RAG, Reasoning, and Agentic Systems
- 2024ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability
- 2025Auditing Meta-Cognitive Hallucinations in Reasoning Large Language Models
- 2025Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinking
- 2026Attribution Techniques for Mitigating Hallucinated Information in RAG Systems: A Survey
- 2024Interpreting and Mitigating Hallucination in MLLMs through Multi-agent Debate