Unverified50% confidenceFactExact time
Large models' failure modes include fabricating facts out of thin air, adjusting answers to迎合 user beliefs, and giving explanations that do not faithfully reflect the generation process, and these require different testing methods.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedIn benchmark testing, execution failures and model decisions should be counted separately to avoid misinterpreting a single quota overrun as a deficiency in model capability.80% similarUnverifiedInstant content generation by large models has quality fluctuation problems, and may generate questions with factual errors, logical contradictions or jumps in difficulty, and multiple generations on the same topic may produce inconsistent results.79% similarUnverifiedMisrouting sends complex requests to small models with insufficient capability, producing answers that appear plausible but are actually incorrect, and the cost of this quality degradation is difficult to quantify.79% similarUnverifiedBias introduced by a single defective data point can leak into seemingly unrelated capability dimensions, causing the model to simultaneously regress across multiple independent metrics such as deceptiveness, compliance, and risk aversion.79% similarUnverifiedCalling real models on every test suite run is an anti-pattern: it is slow, unstable, consumes request quotas, and makes it difficult to distinguish between code issues and changes in model responses when tests fail.79% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1072628API
curl https://kongchang.com/api/v1/knowledge/claims/1072628MCP
get_claim(id=1072628)