Large Language Models (LLMs) like ChatGPT often "hallucinate" by generating plausible-sounding but incorrect information because their core function is to predict the most likely next word based on training data patterns, rather than accessing verified facts. By default, these models lack an "I don't know" response mechanism, leading them to fabricate answers when they lack specific knowledge. Techniques to mitigate this include grounding the LLM with relevant documents using retrieval-augmented generation (RAG), explicitly allowing the model to state uncertainty, and lowering the "temperature" parameter for factual tasks. AI
IMPACT Understanding LLM hallucination is crucial for reliable AI deployment and user trust.
RANK_REASON Explains a core limitation of LLMs and suggests mitigation techniques.
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