[cdchn]

I tried asking it "what is the difference between the palm language model and the bard language model?" and its reply started off "The main difference between the Palm language model and the Bard language model is the size of the dataset they are trained on. Palm is trained on a dataset of 400 billion parameters, while Bard is trained on a dataset of 540 billion parameters." Which to me is even more interesting that what the OP commenter asserted.

[knaik94]

It makes up those numbers, I asked about the difference between the small and large PaLM 2 data set size, and it asserted the small model was trained on 540 billion and the large model was trained on 540 trillion. A different draft instead specified 1.4 trillion for the large.

[cdchn]

It even gave me a table with a whole bunch of differences. All thats made up?

Here is a table that summarizes the key differences between the two language models:

Feature Palm Bard Number of parameters 400 billion 540 billion Vocabulary size 137 billion words 1.5 trillion words

[knaik94]

I didn't mean to argue that everything generated is incorrect. But in my experience, the numbers it generates seem closer to random guesses. If you ask it enough times, it sometimes converges on a number, but I don't think that means it's an accurate value. I was able to make it generate a similar table for the different PaLM 2 sizes, and laMDA, and it listed, PaLM 2 Gecko 137 billion, PaLM 2 Otter 540 billion, PaLM 2 Bison 1.8 trillion, PaLM 2 Unicorn 5.4 trillion, LaMDA 137 billion. For Unicorn, it also lists "Still under development."

Edit: Playing around with it more and it listed WuDao 2.0 1.75 Trillion, Chinchilla 175B, Codex 175B, Dalle2 1.3B, GPT4 1.75T, GPT3.5 540B, GPT3 175B, GPT2 1.37B, GPT 1.3B.

But in the previous question it listed GPT4 540 billion and Codex 5.4 trillion among other contradictions.