A large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content. The term generative AI also is closely connected with LLMs, which are, in fact, a type of generative AI that has been specifically architected to help generate text-based content.
Here is a video that explains the different areas where Bard, ChatGPT, Bing and Claude excel.
Here are some of the people mentioned in the video:
Ethan Mollick @emollick, Yam Peleg @Yampeleg Sundar Pichai @sundarpichai Joel Dean @sirjoeldean Moritz Kremb @moritzkremb
Which model should you use?
The AI wars TL;DR:
– Long context tasks -> Use Claude 2.0.
– Internet required tasks -> Use Bard.
– Harder reasoning tasks -> Use GPT-4.
– Anything with code -> Use Code Interpreter.
– Long (~essay) + Internet -> Use Bing.
And all are crazy good at…
— Yam Peleg (@Yampeleg) July 14, 2023
Bard just converted this image into code.
Things are moving fast… pic.twitter.com/l1A6ATJEOS
— Yam Peleg (@Yampeleg) July 16, 2023
I used Bard to help me with an outline of the different sequences of this video:
Introduction (0:00 – 1:22)
The video begins by introducing the four LLMs that will be compared in the video: Bard, ChatGPT, Bing, and Claude. Bard is a large language model from Google AI, ChatGPT is a large language model from OpenAI, Bing is a large language model from Microsoft, and Claude is a large language model from Anthropic. The video then discusses the different strengths and weaknesses of each LLM.
Long Context Tasks (1:23 – 4:16)
The next sequence of the video compares the LLMs on long context tasks. Long context tasks are tasks that require the LLM to understand and respond to a large amount of text. Examples of long context tasks include summarizing a factual text or creating a story. The video finds that Claude 2.0 is the best LLM for long context tasks, followed by Bard and ChatGPT.
Internet Required Tasks (4:17 – 6:33)
The following sequence of the video compares the LLMs on internet required tasks. Internet required tasks are tasks that require the LLM to access and process information from the internet. Examples of internet required tasks include searching for information or translating languages. The video finds that Bard is the best LLM for internet required tasks, followed by Claude 2.0 and Bing.
Harder Reasoning Tasks (6:34 – 7:19)
The next sequence of the video compares the LLMs on harder reasoning tasks. Harder reasoning tasks are tasks that require the LLM to understand and respond to complex questions or requests. Examples of harder reasoning tasks include answering open ended questions or writing different kinds of creative content. The video finds that GPT-4 is the best LLM for harder reasoning tasks, followed by Bard and Claude 2.0.
Anything with Code (7:20 – 10:19)
The following sequence of the video compares the LLMs on tasks involving code or code interpretation. Tasks involving code or code interpretation require the LLM to understand and process code. Examples of tasks involving code or code interpretation include writing code, debugging code, or generating documentation. The video finds that GPT-4 with an internet connection is the best LLM for tasks involving code, followed by Bard and Claude 2.0.
Personal AI (10:20 – 12:31)
The next sequence of the video discusses the potential of LLMs to be used as personal AI assistants. Personal AI assistants are AI programs that can help users with a variety of tasks, such as setting reminders, scheduling appointments, or providing information. The video finds that all four LLMs have the potential to be used as personal AI assistants, but Bard and Claude 2.0 may be better suited for this role due to their ability to understand and respond to natural language.
Quivr (12:32 – 13:49)
The following sequence of the video introduces Quivr, a new tool that allows users to compare the performance of different LLMs on a variety of tasks. Quivr is a web-based tool that allows users to upload text prompts and compare the results of different LLMs. The video demonstrates how Quivr can be used to compare the performance of Bard, ChatGPT, Bing, and Claude 2.0 on different tasks.
Conclusion (13:50 – 14:22)
The final sequence of the video summarizes the key points of the video and provides recommendations for choosing the right LLM for a particular task. The video recommends choosing the LLM that is best suited for the specific task at hand. The video also recommends using Quivr to compare the performance of different LLMs before making a decision.