Summary of Phi-4 Technical Report, by Marah Abdin et al.

Phi-4 Technical Report

by Marah Abdin, Jyoti Aneja, Harkirat Behl, Sébastien Bubeck, Ronen Eldan, Suriya Gunasekar, Michael Harrison, Russell J. Hewett, Mojan Javaheripi, Piero Kauffmann, James R. Lee, Yin Tat Lee, Yuanzhi Li, Weishung Liu, Caio C. T. Mendes, Anh Nguyen, Eric Price, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Xin Wang, Rachel Ward, Yue Wu, Dingli Yu, Cyril Zhang, Yi Zhang

First submitted to arxiv on: 12 Dec 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary A novel 14-billion parameter language model, phi-4, is introduced, which deviates from traditional pre-training methods by incorporating synthetic data throughout its training process. This approach enables phi-4 to surpass its teacher model, GPT-4, on STEM-focused QA capabilities. The model’s performance is attributed to improved data quality, a revised training curriculum, and innovations in the post-training scheme.
Low	GrooveSquid.com (original content)	Low Difficulty Summary phi-4 is a big language model that helps machines understand human language better. Unlike other models, phi-4 uses made-up data to learn from. This lets it do a lot of things that others can’t, like answer science questions really well. The model’s creators say that their unique approach to training and the quality of the data they use are what make phi-4 so good.