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LLaMA: Open and Efficient Foundation Language Models research paper by Meta, 2023

Meta · Feb 27, 2023 · Foundation models · 21,675 citations · 25 upvotes

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What it shows

Open models from 7B to 65B trained only on public data; the 13B model beats GPT-3 on most benchmarks.

Summarised by hand from the abstract.

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Language Models are Few-Shot LearnersGPT-3, a 175B-parameter model, does new tasks from a few examples in the prompt, with no fine-tuning.OpenAI · May 202063.6k
GPT-4 Technical ReportA multimodal model that passes a simulated bar exam with a score around the top 10% of test takers.OpenAI · Mar 202327.3k
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextRecalls details across millions of tokens of text, hours of video and days of audio.Google DeepMind · Mar 20244,002
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your PhoneSmall models trained on filtered and synthetic data that run on a phone and rival much larger ones.Microsoft · Apr 20242,497
Nemotron-4 340B Technical ReportOpen models sized for one 8-GPU server, aligned with over 98% synthetic data.NVIDIA · Jun 2024138
About this paper
Authors
Hugo Touvron, Thibaut Lavril, Gautier Izacard and 11 more
arXiv
2302.13971 · PDF
Venue
arXiv.org
Citations
21,675, 2,221 influential · Semantic Scholar
Upvotes
25 · Hugging Face
Lab
Meta · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

What changed
New paperAdded to the listSep 24, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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