Verified65% confidenceFactExact time
In the absence of clear constraints, AI tends to fill in gaps with statistically most plausible content rather than proactively asking for clarification, which is an extension of the hallucination problem.
3
Sources
65%
Confidence
Long-term
Relevance
7/23/2026
First Seen
Sources
Related Claims
VerifiedAI hallucination is not a random failure but an inevitable result of low-quality context input; when the input context is sparse, the model lacks a handle to anchor itself to real information.84% similarUnverifiedAI在拆分需求的同时进行需求评审，不主动修改需求，而是标记表述不明确、存在歧义的地方81% similarUnverifiedIn the field of AI safety, 'being able to report gaps' is often called honesty or calibration; a model that only pursues 'always answering when asked' tends to fill in blanks with plausible guesses when facing missing information, and this is precisely the source of hallucinations.79% similarVerifiedThe accuracy of requirement descriptions is crucial when collaborating with AI, as vague descriptions cause the AI to waste inference resources in the wrong direction.79% similarVerifiedThe vast majority of AI risk disclosures lack substantiveness and are more boilerplate or compliance-oriented in nature.78% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/598582API
curl https://kongchang.com/api/v1/knowledge/claims/598582MCP
get_claim(id=598582)