Unverified50% confidenceFactExact time
Large language models are essentially probabilistic generative engines based on the Transformer architecture; at runtime their inputs and outputs are limited to sequences of text tokens, and they cannot directly read local files, query databases, call APIs, or manipulate browsers.
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9/7/2026
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Related Claims
VerifiedLarge language models are based on the Transformer architecture and are trained on massive amounts of text data.77% similarVerifiedBefore the advent of Transformer, the field of natural language processing relied primarily on RNNs and LSTMs to process sequential data, architectures that had to process text word by word in sequence and could not perform parallel computation74% similarUnverifiedThe 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.74% similarVerified基于Transformer的大语言模型使用注意力机制将系统提示、用户输入和外部检索内容统一编码为同质化的token序列，模型无法在语义层面区分可信指令与不可信数据74% similarUnverifiedThe Transformer architecture underlying large language models does not directly process raw characters; instead, it first uses a tokenizer to split text into subword units, which are then mapped to high-dimensional vectors for computation.73% similar
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