Unverified50% confidenceFactExact time
Each model was paired with four prompt types—zero-shot, few-shot, chain-of-thought, and few-shot plus chain-of-thought—each in two groups, producing approximately 52,000 labels in total.
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50%
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Long-term
Relevance
10/5/2026
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UnverifiedIn few-shot scenarios (e.g., approximately 20 samples per class), multi-class subdivision should be abandoned, all defects should be collapsed into a binary classification problem (defective/non-defective), and specific classification should be left to manual post-processing.68% similarUnverifiedThe model places n-gram lookup tables at layers 1 and 14, with 24 hashes each indexing approximately 16 million rows, totaling 196 billion parameters, exceeding one-third of the model's weights.66% similarUnverifiedThe 2023 Microsoft Research paper 'LIMA: Less Is More for Alignment' showed that using only 1,000 carefully curated high-quality samples can enable a base model to exhibit conversational capabilities comparable to fine-tuning on tens of thousands of samples.65% similarVerifiedBefore outputting a final answer, a reasoning model's internal chain of thought may consume 3,000 to 20,000 tokens, far exceeding the approximately 500 tokens of an ordinary model.65% similar
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