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LLaMA: Open and Efficient Foundation Language Models

Touvron et al., 2023, LLaMA (arXiv preprint arXiv:2302.13971)

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3.6 Massive Multitask Language Understanding

  1. Where did LLaMA-65B lag behind, and what explanation is offered?
    On MMLU (5-shot) it trailed Chinchilla-70B and PaLM-540B by a few points across most domains, probably because its books and academic papers totalled only 177 GB versus up to 2 TB of books for those models.
    • Touvron et al., 2023, LLaMA (arXiv preprint arXiv:2302.13971), §3.6

Source

  • Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave and Guillaume Lample. 2023. LLaMA: Open and Efficient Foundation Language Models. In arXiv preprint arXiv:2302.13971. https://arxiv.org/abs/2302.13971