Unverified50% confidenceFactExact time
Embedding models still generate vectors for grammatically broken text that are close to those for normal contract sentences, because they essentially statistically model character co-occurrence patterns rather than checking grammar.
1
Sources
50%
Confidence
Long-term
Relevance
10/13/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedThe output of large language models is fundamentally probabilistic text generation, and a single rephrasing of a prompt can change field names, nesting levels, or enumeration values in the output without triggering compile-time or static analysis warnings.75% similarUnverifiedLarge language models encode textual statistical patterns into parameters via backpropagation and do not store raw data in the traditional sense, but high-frequency text may produce memorization phenomena and be reproduced nearly verbatim.75% similarUnverifiedA model can avoid words or sentence patterns flagged as harmful at the surface output level while retaining the original bias structure in the semantic associations encoded in its weights.74% similarUnverified实验数据显示,语法约束解码使小型语言模型生成Bash代码的语法正确率大幅提高,引号不匹配和结构不完整等错误几乎被完全消除74% similarUnverifiedUsing a language model to handle unstructured merchant description fields for fuzzy matching to accounting categories is more efficient than traditional rule engines, which require extensive manual maintenance of mapping tables.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1019543API
curl https://kongchang.com/api/v1/knowledge/claims/1019543MCP
get_claim(id=1019543)