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If you wanted to keep up to date with upcoming LLM releases, where would you suggest looking?

Generation Details

Date LLM Access Method
Nov 2024 GPT-4 Web UI

Based on the search results and the current landscape of LLM developments, there isn't a single centralized resource that provides a comprehensive overview of upcoming LLM releases from an objective standpoint. However, there are several valuable sources you can use to stay informed about LLM news and upcoming releases:

  1. Online Communities and Forums:
  2. Reddit communities like r/LocalLLaMA are good sources for discussions and news about open-source LLMs.
  3. Hugging Face's model hub (https://huggingface.co/models) is an excellent platform to track new open-source LLM releases and their popularity.
  4. Newsletters:
  5. The Batch by Andrew Ng (deeplearning.ai) offers general ML and AI news, including LLM updates.
  6. AI TLDR (https://tldr.tech/ai) provides general AI news, including LLM developments.
  7. Alphasignal newsletter (https://alphasignal.ai/) focuses on AI and LLM news.
  8. Podcasts:
  9. Latent Space podcast offers in-depth analysis of AI and LLMs.
  10. ThursdAI podcast (https://sub.thursdai.news/podcast) covers AI and LLM topics.
  11. GitHub Repositories:
  12. The repository you mentioned (https://github.com/continuedev/what-llm-to-use) provides an overview of current LLMs, though it may not always be up-to-date with the latest releases.
  13. AI News Websites:
  14. Websites like TechRadar's AI section or MarktechPost often cover new LLM releases and comparisons.
  15. Academic Sources:
  16. ArXiv (https://arxiv.org/) is a good source for pre-print research papers on new LLM architectures and improvements.

To get an overview of what's in the pipeline:

  1. Follow AI research labs and companies on social media platforms like Twitter/X, as they often tease upcoming releases.
  2. Keep an eye on major AI conferences (e.g., NeurIPS, ICML, ACL) where new models and techniques are often presented.
  3. Monitor the GitHub repositories and official blogs of major AI companies and open-source projects for announcements and release schedules.

While these sources can provide a good overview, it's important to note that many companies keep their upcoming releases confidential until close to launch. Additionally, the fast-paced nature of the field means that even recently published information can quickly become outdated.

For objective sources, academic publications, peer-reviewed journals, and independent AI researchers' blogs or social media accounts can offer more neutral perspectives on LLM developments. However, given the competitive nature of the field, truly objective and comprehensive pipeline information is often hard to come by.

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.