Unverified50% confidenceFactExact time
BERT fine-tuning typically requires only hundreds to thousands of samples to achieve good results on downstream classification tasks.
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50%
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Long-term
Relevance
10/5/2026
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UnverifiedFor personal style imitation tasks, a few hundred to a few thousand high-quality, carefully annotated samples often outperform tens of thousands of uncleaned raw records.72% similarUnverifiedIn a corpus containing approximately 1,000 to 2,000 clauses, checking all pairwise combinations would yield candidate pairs numbering in the millions.66% similarUnverifiedA 70-billion-parameter model corresponds to approximately 1.4 trillion training tokens under the Chinchilla optimum61% similarUnverifiedA single detailed tool description may consume hundreds of tokens, and ten tools can easily exceed a fixed overhead of one thousand tokens.61% similarUnverifiedIn autoregressive generation, producing a response containing 500 tokens requires 500 forward passes, which cannot be parallelized; whereas perception tasks such as image classification require only a single forward pass.59% similar
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