This paper proposes I NSTRUCT M INING, a linear rule for evaluating instruction-following data quality, which is formulated using specific natural language indicators and demonstrated that it can help select relatively high-quality samples from various instruction- following datasets. Large language models typically undergo two training stages, pretraining and finetuning. Despite …
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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. Despite advances in finetuning, a standardized guideline for selecting high-quality datasets to optimize this process remains elusive. In this paper, we first propose InstructMining, an …
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Large language models typically undergo two training stages, pretraining and finetuning. Despite that large-scale pretraining endows the model with strong capabilities to generate natural language responses, these pretrained models can still fail to understand human instructions at times. To enhance language models' ability of interpreting and responding to …
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Download Citation | Instruction Mining: High-Quality Instruction Data Selection for Large Language Models | Large language models typically undergo two training stages, pretraining and finetuning.
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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 with humans.
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Large language models (LLMs) have demonstrated transformative capabilities, powering numerous applications with the strong ability in automatically generating responses according to human instructions (Ouyang et al., 2022; Peng et al., 2023; Chung et al., 2022; Scao et al., 2022).However, it is hard sometimes for language models to capture the meaning of human …
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