I don't know how intentional it is / was, but LLMs in general just love to hear themselves talk!

They do, and I want to encourage them to do so because they think through talking. What I don’t want to do is spend time reading all that.

We will probably just get reader-side affordances for this like auto-folded justification and introduction sections and so on.

Doubtless some chat interface will add this the way they’ve added reasoning folding.

Thinking models think through talking, don't reveal that talking, then answer by again thinking through talking. It's kinda funny in a way.

I always expose reasoning traces. How else can you seriously debug?

The closed models aren't giving you the real thinking traces, though.

It’s wild the people don’t want to see the thinking

Those traces are just summaries of the reasoning.

> LLMs in general just love to hear themselves talk!

Because that’s what’s in the training set. Reticent humans don’t have blogs.

Interesting idea. I think they're getting more wordy over time, personally, so I think it's more to do with the training than the raw data.

Is it just a coincidence that the companies creating them charge by the token?

The aligned incentive appears to be realigning in favor of the corporation.

Pray they do not realign them further.

There are times I require single word answers. I will use whatever model responds as I desire and at this point those models are just a few.

The cost-per-task benchmarks align incentives toward more efficient output and those are the ones gaining steam.