Extrapolating from Five Words

If you only get about five words to convey an idea, what will someone extrapolate from those five words? Rather than guess, you can use LLMs to experimentally discover what people are likely think those five words mean. You can use this to iterate on what five words you want to say in order to best convey your intended meaning.

I got this idea because I tried asking Claude to summarize an article at a link. Claude doesn’t follow links, so it instead hallucinated a summary from the title, which was included in the URL path. Here’s an example of it doing this with one of my LessWrong posts:

It hallucinates some wrong details and leaves out lots of details that are actually in the post, but it’s not totally off the mark here. If my ~Five Words were “the problem of the criterion matters”, this would be a reasonable extrapolation of why I would say that.

Rather than using a link, I can also ask Claude to come up what it thinks I would have put in a post with a particular title:

Strangely it does worse here in some ways and better in others. Unlike when it hallucinated the summary of the link, this time it came up with things I would absolutely not say or want someone to come away with, like the idea that we could resolve the problem of the criterion enough to have objective criteria for knowledge.

But maybe prompting it about LessWrong was the issue, since LessWrong puts off a lot of positivists vibes, Eliezer’s claims to the contrary not withstanding. So I tried a different prompt:

This is fine? It’s not great. It sounds like a summary of the kind of essay a bored philosophy undergrad would write for their epistemology class.

Let me try asking it some version of “what do my ~Five Words mean?”:

This is pretty good, and basically what I would expect someone to take away from me saying “the problem of the criterion matters”. Let’s see what happens if I tweak the language:

Neat! It’s picked up on a lot of nuance implied by saying “important” rather than “matters”. This would be useful for trying out different variations on a phrase to see what those small variations change about the implied meaning. I could see this being useful for tasks like word smithing company values and missions and other short phrases where each word has to carry a lot of meaning.

Now let’s see if it can do the task in reverse!

Honestly, “uncertainty undermines knowledge” might be better than anything I’ve ever come up with. Thanks, Claude!

As a final check, can Claude extrapolate from its own summary?

Clearly it’s lost some of the details, particularly about the problem of the criterion, and has made up some things I wasn’t trying to have it get at. Seems par for the course in terms of condensing down a nuanced message into about five words and still having the core of the message conveyed.

Okay, final test, what can Claude extrapolate from typical statements I might make about my favorite topic, fundamental uncertainty?

Hmm, okay, but not great. Maybe I should try to find another phrase to point to my ideas? Let’s see what it thinks about “fundamental uncertainty” as a book title:

Close enough. I probably don’t need to retitle my book, but I might need to work on a good subtitle.

Based on the above experiment in prompt engineering, Claude is reasonably helpful at iterating on summaries of short phrases. It was able to pick up on subtle nuance, and that’s really useful for finding the right short phrase to convey a big idea. The next time I need to construct a short phrase to convey a complex idea, I will likely iterate the wording using Claude or another LLM.