ChatGPT on Open vs. Closed Source Large Language Models (LLMs) for Internal AI Projects
Note from Lionel: The following ChatGPT-authored post is part of our ongoing series about AI and its use in business. This fifth ChatGPT post is a companion piece to the open or closed LLM post I wrote earlier this week. To see other posts in this series, visit the #ForcepointAI tag page. This week, I used Bing Chat Enterprise for this blog post.
Actual prompt: Write an informative blog post for senior executives who are implementing AI projects for their organizations. Explain the benefits and the disadvantages of using open vs. closed source Large Language Models (LLMs) for internal AI projects. Please include a list of benefits and disadvantages for both. Show examples of the most popular open source and closed source LLMs.
Large Language Models (LLMs) have revolutionized natural language processing and AI. They are trained on enormous amounts of text data and can generate text, answer questions, and perform other tasks. When it comes to implementing LLMs for internal AI projects, one important decision to make is whether to use open or closed source LLMs. In this post, we’ll explore the benefits and disadvantages of both options.
Open Source LLMs
Open Source LLMs are language models whose source code is publicly available and can be freely accessed, used, modified, and distributed by anyone.
Benefits of Open Source LLMs
- Control: With open-source LLMs, you have control over the model, its training data, and its applications.
- Customization: Open-source LLMs are easier to run and customize because their underlying architecture and weights are publicly available.
- Community support: Open-source LLMs are often supported by a large community of developers who contribute to their development and improvement.
- Innovation: The open-source community is known for its innovation and ability to quickly adapt to new technologies.
- Transparency: With open-source LLMs, you have full visibility into the model’s inner workings, which can help build trust with your customers.
Disadvantages of Open Source LLMs
- Limited resources: Open-source projects may have limited resources compared to closed-source projects backed by large corporations.
- Dependency on community: The development and improvement of open-source LLMs depend on the contributions of the community, which may not always be reliable.
Some popular open source LLMs include Alpaca1, BLOOM2, BLOOMChat2, Cerebras-GPT2, Dolly2, Falcon2, FastChat2, FLAN-T52, FLAN-UL22, GPT-J2, GPT4All2 and GPTNeo2.
Closed Source LLMs
Closed Source LLMs are language models whose source code is not publicly available. They are often developed by large corporations and may be proprietary.
Benefits of Closed Source LLMs
- Resources: Closed-source LLMs are often backed by large corporations with significant resources for development and improvement.
- Support: Closed-source LLMs may come with dedicated support from the company that developed them.
Disadvantages of Closed Source LLMs
- Limited control: With closed-source LLMs, you have limited control over the model, its training data, and its applications.
- Limited customization: The underlying architecture and weights of closed-source LLMs are not publicly available, making customization and fine-tuning impossible.
- Lack of transparency: With closed-source LLMs, you have limited visibility into the model’s inner workings.
Some popular closed source LLMs include Bard, ChatGPT3, Claude, Cohere and Jurassic.
In conclusion, the decision to use open or closed source LLMs for internal AI projects depends on your specific needs and priorities. Open source LLMs offer control, customization, community support, innovation, and transparency. On the other hand, closed source LLMs may have more resources and dedicated support. It’s important to carefully weigh the benefits and disadvantages of both options before making a decision.
