RESEARCH PAPERSubmitted 27 Feb 2023

LLaMA: Open and Efficient Foundation Language Models

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; Guillaume Lample

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Major category: Components of WAMs
Quadrant: Not applicable
Architecture: Not applicable
Prediction paradigm: Not applicable
Subcategories: Language & vision-language backbones Language & VLM backbones
Source review status: Verified from primary sources

Category review. LLaMA is a general pretrained causal language backbone for next-token modeling. The manuscript explicitly identifies such autoregressive LLMs as language-encoding/task-conditioning components; lack of motor actions is appropriate for this module. Reading evidence

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BibTeX

@article{llama,
      title={LLaMA: Open and Efficient Foundation Language Models}, 
      author={Hugo Touvron and Thibaut Lavril and Gautier Izacard and Xavier Martinet and Marie-Anne Lachaux and Timothée Lacroix and Baptiste Rozière and Naman Goyal and Eric Hambro and Faisal Azhar and Aurelien Rodriguez and Armand Joulin and Edouard Grave and Guillaume Lample},
      year={2023},
      journal={arXiv:2302.13971},
}

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