What does it mean when an LLM “hallucinates” & why do LLMs hallucinate?

Large Language Models (LLMs) such as ChatGPT are powerful tools capable of generating coherent and contextually appropriate text, but they can also produce "hallucinations"—outputs that are factually incorrect or entirely fictional. These hallucinations arise because LLMs are trained on vast amounts of text data, including both factual and fictional content, and they lack access to external ground truth for verification. The models generate text by predicting the next word based on patterns learned during training, prioritizing coherence over factual accuracy. To mitigate such hallucinations, reinforcement learning with human feedback (RLHF) is proposed as a solution, where human evaluators assess the quality of generated text and provide feedback to guide the model towards greater factual accuracy. Fine-tuning techniques, such as domain-specific training, adversarial training, and the use of multi-modal models, are also explored to enhance the reliability of LLM outputs. Despite their potential, LLMs require careful handling to ensure the accuracy of their outputs, and RLHF represents a promising approach to achieve this.

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