Unverified50% confidenceFactExact time
Wake word training is essentially a binary classification problem in keyword spotting (KWS); common lightweight architectures include DS-CNN and LSTM-based variants, which can be compressed to tens of kilobytes for local deployment on MCUs.
1
Sources
50%
Confidence
Long-term
Relevance
10/8/2026
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70% similarUnverifiedThe pretraining and fine-tuning paradigm originated from NLP and computer vision, with its core being large-scale data pretraining to learn general low-level features, and downstream tasks adapted using a small amount of labeled data.69% similarUnverifiedLLM的训练数据包含大量Web开发代码，不加限制时会自然倾向于引入npm包，而这在小程序沙箱环境中会直接导致运行失败67% similarVerifiedFeature stores address the training-serving skew problem through a unified feature definition layer, ensuring that offline training and online serving use consistent feature logic.67% similarUnverifiedSafety classifiers are typically fine-tuned from pre-trained language models such as BERT and RoBERTa, or use specially trained lightweight discriminative models.67% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/987507API
curl https://kongchang.com/api/v1/knowledge/claims/987507MCP
get_claim(id=987507)