Unraveling the Impact of Data Scaling on LLMs

Scaling Laws for Downstream Task Performance of Large Language Models presents an in-depth study of how pretraining data volume and alignment affect the performance of language models on downstream machine translation tasks.

• Investigates the effect of pretraining data size and its alignment with downstream data.
• Shows monotonic improvement in BLEU score and cross-entropy with better alignment and more data.
• Provides predictions for downstream performance based on pretraining data properties.

This research delivers vital insights for selecting pretraining data for transfer learning scenarios, directly impacting the development of LLMs with superior translation abilities, and offers a practical framework for predicting the success of language models in specific tasks.