Unverified50% confidenceFactExact time
大语言模型通常采用仅解码器（Decoder-only）的Transformer变体，通过因果掩码确保每个位置只能关注其之前的token
1
Sources
50%
Confidence
Long-term
Relevance
8/14/2026
First Seen
Sources
Related Claims
UnverifiedTransformer uses Positional Encoding to retain word order information while performing parallel computation.78% similarUnverifiedTransformer-based large language models generate text token by token in an autoregressive manner, with no external symbolic verification system to check whether each reasoning step is valid76% similarVerifiedTransformer-based large language models use attention mechanisms to uniformly encode system prompts, user inputs, and externally retrieved content as homogenized token sequences, making it impossible for the model to distinguish trusted instructions from untrusted data at the semantic level.75% similarUnverifiedModern large language models are all built on the Decoder-only variant of the Transformer, with parameter counts ranging from billions to trillions.75% similarUnverifiedUnlike RNNs, the Transformer does not process sequences step by step and therefore lacks an inherent sense of order, requiring positional information to be injected through Positional Encoding74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/746352API
curl https://kongchang.com/api/v1/knowledge/claims/746352MCP
get_claim(id=746352)