A joint task-model adaptation method learns optimal weights for data selection indicators via ICL proxies on small validation sets, matching or exceeding full-dataset fine-tuning performance with only 30% of samples on GSM8K.
Aligning large language models to follow instructions and hallucinate less via effective data filtering
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Learning Multi-Indicator Weights for Data Selection: A Joint Task-Model Adaptation Framework with Efficient Proxies
A joint task-model adaptation method learns optimal weights for data selection indicators via ICL proxies on small validation sets, matching or exceeding full-dataset fine-tuning performance with only 30% of samples on GSM8K.