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On October 9, 2024, Zeta Alpha announced the top 100 most cited AI papers in 2023. Meta’s publications on LLaMA, Llama 2, and Segment Anything secure the first three spots in AI research.
The paper LLaMA: Open and Efficient Foundation Language Models, which has 8534 citations, introduces a collection of foundation models ranging from 7 billion to 65 billion parameters. The paper also shows that LLaMA-13B outperforms the 175B parameter GPT-3 model on most benchmarks.
Read More: Mistral AI’s New LLM Model Outperforms GPT-3 Model
Ranking second, the Llama 2: Open Foundation and Fine-tuned Chat Models paper has 7,774 citations. It presents a set of pre-trained and fine-tuned language models with parameters ranging from 7B to 70B. These are Llama 2-Chat models specifically optimized for dialogue use cases to enhance conversational AI capabilities.
Gaining the third position, the meta paper on Segment Anything discusses a new model for image segmentation with zero-short performance. The paper also noted that the model is released with a large dataset (SA-1B) of 1B masks and 11M images. This initiative aims to advance research in foundation models for computer vision.
Although Meta’s papers hold the top three positions, Microsoft leads with 13 papers in the overall top 100 compared to Meta’s eight.
Following Microsoft, Stanford University has 11 papers, while Carnegie Mellon University and Google each have 10. This demonstrates a strong presence of different industries in AI research.
For more insights on these rankings, visit the Zeta Alpha post by Mathias Parisol.
