Unverified50% confidenceFactExact time
The LLM training and inference pipeline includes stages such as tokenization, embedding layers, self-attention mechanisms, feed-forward networks, and residual connections.
1
Sources
50%
Confidence
Long-term
Relevance
8/31/2026
First Seen
Sources
Related Entities
Related Claims
Unverified现代LLM通常经过专门的微调来学习何时、如何调用工具，训练数据中包含大量工具使用的示例对话76% similarUnverifiedDuring training, LLMs learn grammatical rules and common patterns from large amounts of code and text data, but design judgment requires the combined capabilities of visual perception, user psychology, and brand consistency.73% similarUnverified当前主流 LLM 采用指令遵循（Instruction Following）训练方式，会用概率最高的方式补全输出72% similarUnverifiedCurrent mainstream LLMs universally adopt a pre-training plus fine-tuning paradigm, first performing unsupervised pre-training on internet-scale corpora, then aligning with human preferences through techniques such as RLHF.72% similarUnverifiedLLM 在 RLHF 训练中被强化了「尽快给出有用输出」的行为倾向，默认帮用户做事优先于和用户对齐认知72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/828536API
curl https://kongchang.com/api/v1/knowledge/claims/828536MCP
get_claim(id=828536)