Verified75% confidenceFactExact time
Large language models carry a hallucination risk in numerical reasoning and may produce errors in scenarios such as aggregate calculations, time-range filtering, or multi-table joins.
3
Sources
75%
Confidence
Long-term
Relevance
9/1/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe larger the parameters of a large language model, the higher the computational requirements for a single forward inference pass, and latency increases accordingly.74% similarUnverified大模型在逆向拆解任务中存在幻觉风险，更容易在常见框架标准用法上准确，而在非标准实现或项目特有约定上产生偏差74% similarUnverified上下文中存在相互冲突或交替出现的格式约束，会显著提升模型的幻觉率和格式错误率74% similarUnverifiedHallucinated proofs differ from rounding errors in numerical computation or obvious error reports in symbolic computation, as the latter two tend to be exposed in subsequent steps, whereas hallucinated proofs may remain undetected until discovered by peer review.74% similarUnverifiedLarge language models are prone to hallucinations when handling complex and ambiguous requirements; the larger and more ambiguous the scope, the higher the probability of AI errors, so reducing scope for MVP lowers the risk of AI mistakes.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/839343API
curl https://kongchang.com/api/v1/knowledge/claims/839343MCP
get_claim(id=839343)