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Large language models (LLMs) are initially pretrained for broad capabilities and then finetuned with instruction-following datasets to improve their performance in interacting with humans.
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INSTRUCTION MINING: INSTRUCTION DATA SELEC-TION FOR TUNING LARGE LANGUAGE MODELS Anonymous authors Paper under double-blind review ABSTRACT Large language models (LLMs) are initially pretrained for broad capabilities and then finetuned with instruction-following datasets to improve their performance in interacting …
Instruction Mining: High-Quality Instruction Data Selection for Large Language Models. 07/12/2023 .
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Instruction Mining: When Data Mining Meets Large Language Model Finetuning . Large language models (LLMs) are initially pretrained for broad capabilities and then finetuned with instruction-following datasets to improve their performance in interacting with humans.
Analyze arXiv paper 2307.06290. LLMs are initially pretrained for broad capabilities and then finetuned with instruction-following datasets to improve their performance in interacting with humans. Despite advances in finetuning, a standardized guideline for selecting high-quality datasets to optimize this process remains elusive.In this paper, we first propose …
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