data + plural = number
  data - plural = research
  king - crown = (didn't work... crown gets circled in red)
  king - princess = emperor
  king - queen = kingdom
  queen - king = worker
  king + queen = queen + king = kingdom
  boy + age = (didn't work... boy gets circled in red)
  man - age = woman
  woman - age = newswoman
  woman + age = adult female body (tied with man)
  girl + age = female child
  girl + old = female child

> king-man+woman=queen

Is the famous example everyone uses when talking about word vectors, but is it actually just very cherry picked?

I.e. are there a great number of other "meaningful" examples like this, or actually the majority of the time you end up with some kind of vaguely tangentially related word when adding and subtracting word vectors.

(Which seems to be what this tool is helping to illustrate, having briefly played with it, and looked at the other comments here.)

(Btw, not saying wordvecs / embeddings aren't extremely useful, just talking about this simplistic arithmetic)

Some of these make more sense than others (and bookshop is hilarious even if it's only the best answer by a small margin; no shade to bookshop owners).

  map - legend = Mercator projection
  noodle - wheat = egg noodle
  noodle - gluten = tagliatelle
  architecture - calculus = architectural style
  answer - question = comment
  shop - income = bookshop
  curry - curry powder = cuisine
  rice - grain = chicken and rice
  rice + chicken = poultry
  milk + cereal = grain
  blue - yellow = Fiji
  blue - Fiji = orange
  blue - Arkansas + Bahamas + Florida - Pluto = Grenada

First off, this interface is very nice and a pleasure to use, congrats!

Are you using word2vec for these, or embeddings from another model?

I also wanted to add some flavor since it looks like many folks in this thread haven't seen something like this - it's been known since 2013 that we can do this (but it's great to remind folks especially with all the "modern" interest in NLP).

It's also known (in some circles!) that a lot of these vector arithmetic things need some tricks to really shine. For example, excluding the words already present in the query[1]. Others in this thread seem surprised at some of the biases present - there's also a long history of work on that [2,3].

[1] https://blog.esciencecenter.nl/king-man-woman-king-9a7fd2935...

[2] https://arxiv.org/abs/1905.09866

[3] https://arxiv.org/abs/1903.03862

Neat! Reminds me of infinite craft

https://neal.fun/infinite-craft/

A few favorites:

wine - beer = grape juice

beer - wine = bowling

astrology - astronomy + mathematics = arithmancy

Reminds me of the very annoying word game https://contexto.me/en/

for founders :

love + time = commitment

boredom + curiosity = exploration

vision + execution = innovation

resilience - fear = courage

ambition + humility = leadership

failure + reflection = learning

knowledge + application = wisdom

feedback + openness = improvement

experience - ego = mastery

idea + validation = product-market fit

Huh, that's strange, I wanted to check whether your embeddings have biases, but I cannot use "white" word at all. So I cannot get answer to "man - white + black = ?".

But if I assume the biased answer and rearrange the operands, I get "man - criminal + black = white". Which clearly shows, how biased your embeddings are!

Funny thing, fixing biases and ways to circumvent the fixes (while keeping good UX) might be much challenging task :)

This is super neat.

I built a game[0] along similar lines, inspired by infinite craft[1].

The idea is that you combine (or subtract) “elements” until you find the goal element.

I’ve had a lot of fun with it, but it often hits the same generated element. Maybe I should update it to use the second (third, etc.) choice, similar to your tool.

[0] https://alchemy.magicloops.app/

[1] https://neal.fun/infinite-craft/

Oh you have all the damn words. Even the Ricky Gervais ones.

I don't get it but I'm not sure I'm supposed to.

    life + death = mortality
    life - death = lifestyle

    drug + time = occasion
    drug - time = narcotic

    art + artist + money = creativity
    art + artist - money = muse

    happiness + politics = contentment
    happiness + art      = gladness
    happiness + money    = joy
    happiness + love     = joy

I've tried to get to "garage", but failed at a few attempts, ChatGPT's ideas also seemed reasonable, but failed. Any takers? :)

Here's a challenge: find something to subtract from "hammer" which does not result in a word that has "gun" as a substring. I've been unsuccessful so far.

As you might expect from a system with knowledge of word relations but without understanding or a model of the world, this generates gibberish which occasionally sounds interesting.

These are pretty good results. I messed around with a dumber and more naive version of this a few years ago[1], and it wasn't easy to get sensinble output most of the time.

[1]: https://github.com/GrantMoyer/word_alignment

This might be helpful: I haven't implemented it in the UI, but from the API response you can see what the word definitions are, both for the input and the output. If the output has homographs, likeliness is split per definition, but the UI only shows the best one.

Also, if it gets buried in comments, proper nouns need to be capitalized (Paris-France+Germany).

I am planning on patching up the UI based on your feedback.

I've always wondered if there's s way to find which vectors are most important in a model like this. The gender vector man-woman or woman-man is the one always used in examples, since English has many gendered terms, but I wonder if it's possible to generate these pairs given the data. Maybe to list all differences of pairs of vectors, and see if there are any clusters. I imagine some grammatical features would show up, like the plurality vector people-person, or the past tense vector walked-walk, but maybe there would be some that are surprisingly common but don't seem to map cleanly to an obvious concept.

Or maybe they would all be completely inscrutable and man-woman would be like the 50th strongest result.

"man-intelligence=woman" is a particularly interesting result.

This is super fun. Offering the ranked matches makes it significantly more engaging than just showing the final result.

Interesting: parent + male = female (83%)

Can not personally find the connection here, was expecting father or something.

doesn’t do anything on my iphone

There was a site like this a few years ago (before all the LLM stuff kicked off) that had this and other NLP functionality. Styling was grey and basic. That’s all I remember.

I’ve been unable to find it since. Does anyone know which site I’m thinking of?

What about starting with the result and finding set of words that when summed together give that result?

That could be seen as trying to find the true "meaning" of a word.

artificial intelligence - bullsh*t = computer science (34%)

Just use a LLM api to generate results, it will be far better and more accurate than a weird home cooked algorithm

What does it mean when it surrounds a word in red? Is this signalling an error?

cool but not enough data to be useful yet I guess. Most of mine either didn't have the words or were a few % off the answer, vehicle - road + ocean gave me hydrosphere, but the other options below were boat, ship, etc. Klimt almost made it from Mozart - music + painting. doctor - hospital + school = teacher, nailed it.

Getting to cornbread elegantly has been challenging.

shows how bad embeddings are in a practical way

potato + microwave = potato tree

I tried:

-red

and:

red-red-red

But it did not work and did not get any response. Maybe I am stupid but should this not work?

dog - cat = paleolith

paleolith + cat = Paleolithic Age

paleolith + dog = Paleolithic Age

paleolith - cat = neolith

paleolith - dog = hand ax

cat - dog = meow

Wonder if some of the math is off or I am not using this properly

goshawk-cocaine = gyrfalcon , which is funny if you know anything about goshawks and gyrfalcons

(Goshawks are very intense, gyrs tend to be leisurely in flight.)

London-England+France=Maupassant

man - courage = husband

fluid + liquid = solid (85%) -- didn't expect that

blue + red = yellow (87%) -- rgb, neat

black + {red,blue,yellow,green} = white 83% -- weird

mathematics - Santa Claus = applied mathematics

hacker - code = professional golf

Really?!

  man - brain = woman
  woman - brain = businesswoman

wine - alcohol = grape juice (32%)

Accurate.

uncle + aunt = great-uncle (91%)

great idea, but I find the results unamusing

man - intelligence = woman (36%)

woman + intelligence = man (77%)

Oof.

doctor - man + woman = medical practitioner

Good to understand this bias before blindly applying these models (Yes- doctor is gender neutral - even women can be doctors!!)

king - man + woman = queen

queen - woman + man = drone

horse+man

78% male horse 72% horseman

dog+woman = man

That's weird.

King-man+woman=Navratilova, who is apparently a Czech tennis player. Apparently, it's very case-sensitive. Cool idea!

Woman + president = man

dog - fur = Aegean civilization (22%)

huh

male + age = female

female + age = male

rice + fish = fish meat

rice + fish + raw = meat

hahaha... I JUST WANT SUSHI!

man + woman = adult female body

it doesn't know the word human

twelve-ten+five=

six (84%)

Close enough I suppose

three + two = four (90%)

I'm getting Navralitova instead of queen. And can't get other words to work, I get red circles or no answer at all.

noodle+tomato=pasta

this is pretty fun

The app produces nonsense ... such as quantum - superposition = quantum theory !!!

garden + sin = gardening

hmm...

colorless+green+ideas doesn't produce anything of interest, which is disappointing.

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Can someone explain me what the fuck this is supposed to be!?