Best of both worlds: fine-tuned small models complement LLMs for cost and performance benefits
Read the original at arxiv.org→arXiv:2610.24260v1 Announce Type: new Abstract: BERT-based models require thousands of labeled samples to train. Larger models like GPT-X require only 1-5 examples per class. However, performance, cost and time...
Original headline: "Best of Both Worlds: Fine-Tuned Small Models Complement LLMs for Cost and Performance Benefits"
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- Oct 20, 04:00 UTC arXiv cs.CL lead source Best of Both Worlds: Fine-Tuned Small Models Complement LLMs for Cost and Performance Benefits