Tech in Five: Why AI Hallucinates

Key insights

• AI hallucination: Generative models sometimes produce confident but ungrounded content—answers that sound plausible but aren’t supported by the prompt or real-world facts.
  Microsoft frames this as a reliability issue to manage, not just a one-off error.
• Prediction, not knowledge: Large language models generate the next likely token based on patterns in training text; they do not look up facts in a built-in database.
  This probabilistic process can produce invented details that fit the pattern but are false.
• Training-data limits: Gaps, bias, staleness, or poor-quality examples in training data make models more likely to invent information.
  Missing or outdated facts increase hallucination risk.
• Prompt ambiguity: Vague or contradictory prompts raise the chance of made-up answers because the model fills gaps with plausible content.
  Clear, specific prompts reduce uncertainty and improve accuracy.
• Decoding settings: Sampling choices (temperature, top-k/top-p) and model behavior affect creativity versus reliability.
  More conservative decoding reduces hallucinations but can make output less varied.
• Grounding and verification: Microsoft recommends platform controls and user workflows—grounding outputs with sources, traceability, and review—to reduce hallucinations.
  Design systems that show when output is verified, enable checks, and use platform-level mitigations rather than relying only on manual prompts.

Keywords

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