AI hallucinations: Why LLMs make things up (and how to fix it)

What “hallucination” means

• Many argue hallucinations are not a special mode but simply “outputs not fit for purpose” from a probabilistic text generator; in a sense, all outputs are hallucinations, some just align with reality.
• Others insist hallucinations should be reserved for fabricated or non‑grounded claims, especially invented citations, APIs, or features.
• Some frame LLMs as modeling language, not the world; grammar and style are accurate even when facts are wrong.

Can hallucinations be fixed or only mitigated?

• Strong view: hallucinations are inevitable in this architecture; they’re a property of probabilistic modeling and imperfect data, so they can only be managed, not eliminated.
• Counter‑view: calling them “inevitable” is premature; better architectures, confidence estimation, and multi‑stage checking may drastically reduce them.
• Debate over whether hallucinations are “bugs”: some say yes (result not matching user intent), others say no (it’s expected behavior, like Bloom filter false positives or network latency).

Techniques to reduce hallucinations

• Retrieval‑augmented generation (RAG) and domain‑restricted context: treat the LLM as a natural‑language interface over vetted documents; separate fact retrieval from wording.
• External verification and secondary models: fact‑check outputs against context, or use trained “factuality” checkers.
• Prompting and instruction: explicitly discourage making things up, use chain‑of‑thought, or force tool use (e.g., code execution).
• Sampling changes: newer truncation samplers (e.g., min_p, entropy‑based methods) aim to exploit logprob signals to avoid unlikely, error‑prone continuations.

Confidence, uncertainty, and self‑knowledge

• Several comments stress the need for reliable “I don’t know” behavior; others note current confidence is about token likelihood, not ground‑truth.
• Some research suggests internal states encode truthfulness cues that can be probed, but this is still dataset‑ and method‑dependent and far from a general solution.

Use cases, risk tolerance, and product fit

• For low‑stakes tasks (summaries, drafts, simple Q&A), current error rates are seen as comparable to or better than many humans.
• For high‑stakes domains (law, medicine, safety), hallucinations are considered unacceptable unless outputs are mediated by trained professionals and rigorous checks.
• Distinction emphasized between “LLM-as-pattern-generator” and “commercial Q&A service”: in the latter, hallucinations are plainly product failures.

Terminology and user expectations

• “Hallucination” as a term is contested; alternatives suggested include “error,” “inaccuracy,” or “confabulation.”
• Some see anthropomorphic language as misleading; others see it as necessary shorthand for non‑experts.
• A recurring concern is that users over‑trust fluent text; traditional cues for effort and competence are now cheaply faked, so education and UI affordances (e.g., uncertainty indicators) are seen as crucial.