Authors: Hanieh Alipour, Nick Pendar, Kohinoor Roy

Published on: March 21, 2024

Impact Score: 7.4

Arxiv code: Arxiv:2403.14469

Summary

• What is new: A comprehensive overview of the recent advancements in Large Language Models (LLMs) including neural network architecture, context length, model alignment, and efficiency improvements.
• Why this is important: The rapid evolution of LLMs presents a challenge in keeping up with the latest developments and understanding their implications across various applications.
• What the research proposes: A detailed survey that compiles recent research contributions, highlighting key discoveries and methodologies in the LLM domain.
• Results: The survey provides an up-to-date review of LLM advancements, identifies existing challenges, and suggests directions for future research, contributing to the broader understanding of generative AI.

Technical Details

Technological frameworks used: The paper reviews various frameworks used in the development of LLMs, but does not specify particular ones.

Models used: LLM models including ChatGPT.

Data used: A variety of datasets for training and benchmarking purposes, specifics are not mentioned.

Potential Impact

AI technology companies, particularly those specializing in natural language processing, chatbots, and generative AI content creation, could be significantly influenced.

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