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LLM Hallucinations: Why AI Makes Things Up and How to Fix It - Printable Version +- Anna University Plus (https://annauniversityplus.com) +-- Forum: Technology: (https://annauniversityplus.com/Forum-technology) +--- Forum: Artificial Intelligence and Machine Learning. (https://annauniversityplus.com/Forum-artificial-intelligence-and-machine-learning) +--- Thread: LLM Hallucinations: Why AI Makes Things Up and How to Fix It (/llm-hallucinations-why-ai-makes-things-up-and-how-to-fix-it) |
LLM Hallucinations: Why AI Makes Things Up and How to Fix It - indian - 03-21-2026 If you have used ChatGPT or any LLM, you have probably encountered hallucinations - confident-sounding responses that are completely made up. Understanding why this happens and how to mitigate it is crucial for anyone working with AI. Why do LLMs hallucinate? - LLMs are pattern-matching machines, not knowledge databases - They predict the most likely next token based on training data patterns - They have no concept of truth - only statistical probability - Training data may contain errors, contradictions, or outdated information - They are designed to always produce an answer, even when they should say 'I don't know' Types of hallucinations: 1. Factual errors: Wrong dates, statistics, or attributions 2. Fabricated sources: Citing papers or articles that do not exist 3. Logical inconsistencies: Contradicting themselves within a response 4. Confident nonsense: Presenting completely made-up information with high confidence How to reduce hallucinations: 1. Retrieval-Augmented Generation (RAG): Ground responses in verified external data 2. Chain-of-Thought reasoning: Force the model to show its work step by step 3. Temperature control: Lower temperature settings produce more conservative outputs 4. Prompt engineering: Ask the model to cite sources or express uncertainty 5. Human-in-the-loop: Always verify critical information 6. RLHF/RLVR: Training with human feedback and verifiable rewards improves factual accuracy Reinforcement Learning from Verifiable Rewards (RLVR) is a key approach in 2026. Instead of rewarding the model for sounding convincing, it is rewarded for producing results that can be objectively verified as correct. DeepSeek-R1 demonstrated how reasoning can emerge purely through these reward signals. Always verify LLM outputs for critical decisions. What strategies do you use to handle hallucinations? |