Hallucinations in LLMs: What are they and what causes them (svana.name)

On February 24, 2025, a federal court sanctioned three lawyers for referencing fictitious cases generated by an AI tool, highlighting the dangers of "hallucinations" in large language models (LLMs) like GPT. Hallucinations occur when these models produce confident yet factually incorrect or incoherent responses. The article explores various forms of hallucinations, emphasizing their potential harm due to misleading outputs that can influence important decisions. This investigation reveals that hallucinations can stem from diverse causes including faulty training data, computational limitations, and user prompts. For instance, training data may contain inaccuracies or biases, and computational challenges can lead to outputs that don't align with user expectations. The authors argue that while hallucinations might not be entirely unavoidable, understanding their underlying mechanisms is crucial for improving the reliability of AI systems. This research is significant for the AI/ML community as it delineates types of hallucinations and the factors contributing to them, paving the way for more effective detection and mitigation strategies.