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The transductive paradigm refers to a model directly generating answers in a token-by-token completion manner, continuing word by word based on the existing context.
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10/11/2026
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UnverifiedWord-by-word generation is technically called autoregressive generation, where the token generated at each step is appended back to the input sequence as the condition for the next prediction77% similarUnverifiedThe core idea of this method is that after an LLM provides an answer and explanation, concepts not mentioned in the explanation are removed from the original input, and the model is re-queried with the simplified input.77% similarUnverifiedThe core mechanism of automatic prompt tuning is having the model continuously evaluate and revise its own outputs and prompts through multi-round interactions.75% similarUnverifiedCertain specific phrasings in the original prompt activate shortcuts entrenched during model training, causing the model to apply fixed patterns in its responses; after paraphrasing, the model is forced to perform more general reasoning, thereby improving output quality.74% similarUnverifiedWhen a prompt is entered, the model predicts the most likely next token based on context, rather than truly understanding the semantics.72% similar
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