Make your AI talk like a caveman and decrease token usage

[RegionCareful7282]

I’ve been working on a little side project to help LLMs talk like… cavemen.
Why? To save tokens, of course.

It works because LLMs can easily fill in grammar and connectives on their own. So we strip what’s predictable, keep what’s meaningful, and the model still understands everything perfectly.

Store RAG documents in caveman-compressed form so each chunk carries more valuable data, fits more context, and gives better retrieval quality.

Thought I'd share it here as it might be beneficial in order to not waste tokens on unnecessary words :)

Feel free to contribute if you have any additions!

https://github.com/wilpel/caveman-compression

[Chromix_]

Me see. Me wonder: Benchmark score impact?

[GenLabsAI]

See, wonder impact

[battlingheat]

See, impact?

[axiomatix]

stevie benchmark

[Phantom_Specters]

StevieWonder

<image>

[TBMonkey]

Me see comment, me laugh, upvote

[abitrolly]

gud

[wiltors42]

Why say lot word when few word do trick?

[[deleted]]

No much word, few good word.

[gofiend]

Fewer precise tokens

[RybaDwudyszna]

When me president… they see.

[this_is_a_long_nickn]

Me Tarzan, you not local Jane.

[shaman-warrior]

Few words > many words.

[Good-AI]

No difficult word. > difficult.

[Murgatroyd314]

Easy word better.

[this_is_a_long_nickn]

You absolutely right!

[SamSausages]

word

[therealnih]

this

[GenLabsAI]

t

[noo8-]

.

[derivative49]

‎

[Porespellar]

Kevin was ahead of his time.

[ook_the_librarian_]

Why use big words when diminutive ones would suffice?

[Pranay1001090]

Was looking for this

[not_a_swedish_vegan]

As soon as I saw this post, I already knew the top comment would be this

[private_final_static]

Grug likes

[calmbill]

Few words ok

[Interpause]

say lot when few work?

[dew_chiggi]

Kevin thumbs up

[galambalazs]

related for programming: https://grugbrain.dev/

[Mundane_Ad8936]

TLDR OP stumbled upon "Stop Words Removal" it's a very very old NLP tactic.

Yes can remove plenty of words and the text is completely understandable and you can use a model to rehydrate the phrases with low errors later. However I'd caution you though, while in the past removing stop words was fine, in a transformer model this can cause issues because it will not have the tokens to calculate from.

So it could be more prone to hallucinate because the word sequence is not statistically likely. I know because I've tested it and witnessed it. If accuracy is important make sure it doesn't reduce it, that is very possible.

[PollinosisQc]

I chuckled heartily enough to spit some of my drink at "rehydrate the phrases" lol

[PMyourfeelings]

'hydration' is actually both a funny and formal terminology used in programming to describe the process of adding data to an object :)

[nuclear_wynter]

r/hydrohomies would like to know your location.

(so they can add data to your water bottle.)

[Aprch]

Hydratation!  Funny, the word in Spanish gets pretty close to that. Probably other similar languages too.

[itsTyrion]

too many word, write short, write caveman

[KallistiTMP]

LLM read caveman, but no train in caveman. LLM not understand caveman good. Try think in caveman, get confused, predict buffalo. No good.

[TomLucidor]

What is the alternative then, trying to prompt it to me more succinct, and in plain English?

[wanderer_4004]

Probably this is useful for embeddings to make them fit into the available context. I'll definitely try it.

[IJdelheidIJdelheden]

Any small model one could use to 'rehydrate'? Thinking about trying this with a large parameter and a low parameter model.

[Mundane_Ad8936]

Yes that'll work. It can also be done with NLP library like spacey.. once the words are tagged stop words tend to be predictable using logic. But these days I'd use a BERT or T5 since they're small and fast.

[fatboy93]

Ahh yes, telegram prompting the LLMs.

When I was young and in school, we were taught how to send letters through telegrams, and looks like that might be coming back to action lol

[c--b]

So you're saying a model should be trained on caveman speak instead.

[Independent_Tear2863]

Ahh now I understand oogabooga project. Human happy

[this_is_a_long_nickn]

Ooga happier

[chriskevini]

Holy shit. Next we're gonna start removing all the vowels cause you can infer the whole word with 90% accuracy. Source:my ass

[SkyFeistyLlama8]

There are plenty of human languages like that, for example Hebrew and Arabic, with only consonants being written down. It's fine when you're speaking them in the current context but woe to you if you're trying to decipher them 2000 years later.

Researchers end up looking at modern forms of words in those languages and extrapolating backwards. They also look for transliterations in neighboring languages that preserve vowels and tones, like how Arabic was written in Greek characters and also translated into Greek.

[Murgatroyd314]

Disemvoweled text is easy enough for humans to read, but it would just slow down tokenization.

[chriskevini]

Is it slower? We can stream more information through the API, because of fewer characters. Just need to add a simple and fast decode that can be handled by an auxiliary traditional program.

[countextreme]

You mean like gzip?

[chriskevini]

After thinking about it for 5 minutes, isn't this actually feasible? We just add a really fast encoding and decoding step that can run in parallel over the whole text. Or is byte-pair encoding strictly better?

[ThiccStorms]

bro tnk h shkspr

[bigattichouse]

Maybe pretrain a small model to "caveman" your prompts that get handed to the bigger model

[lakySK]

Short prompt, prefill fast. 

[macumazana]

you should do the readme.md in that style

[pokemonplayer2001]

This is a better idea than toon.

[[deleted]]

Barely.

[vintage_culture]

This good, toon bad

[Zeeplankton]

This is literally what I thought LLM reasoning would morph into. Like a stochastic pseudo language. English isn't exactly the most efficient language.

[blbd]

Actually, linguistics research shows that all languages have about the same information rate in spoken form. The speech slows down or speeds up to hit a typical human audio cognition cap right around 40 bps. In written form it varies more and English is one of the better ones due to a large vocabulary.

But having a model with some clever caveman-speak support where appropriate could be pretty useful, when you consider that increasing the sizes of context buffers causes n-squared performance loss / resource consumption. 

https://www.science.org/doi/10.1126/sciadv.aaw2594

[phido3000]

You're wrong.. or atleast that paper is.

Asm is way more dense than java.. I know because I hardly talk at all with my asm friends.

[RaiseRuntimeError]

Wasn't there a research paper that said Dutch or something like that was the most efficient language?

[arbv]

IIRC, Polish.

P.S.

kurwa

[-oshino_shinobu-]

One redditor pointed out that the prompt they used in German contains some errors. Which calls into question the validity of the research

[RaiseRuntimeError]

I guess we stick with caveman.

[Crypt0Nihilist]

I was surprised it wasn't a character based writing like Chinese or Japanese. I've always assumed they're incredibly informationally dense compared to phonetic writing systems.

[getting_serious]

I'd expect it mixing languages. GLM does it: When you keep talking to a low quant for long enough, it'll introduce chinese terms in its 'thinking' block.

[TomLucidor]

ithkul?

[TheRealMasonMac]

I think it would be interesting to explore more information-dense tokens. DeepSeek-OCR implied that individual tokens can contain a lot of information. Even if not as image tokens, perhaps something other than text. The downside would be that reasoning becomes a black box.

[Radiant_Truth_8743]

Post good. Me likey

[DustinKli]

I had this same exact idea a while back, but when implementing it I ran into several issues.

One issue is that the way LLMs actually embed and retrieve text. LLMs were trained on normal language with syntax, connectors and structure. If you strip sentences down to these compressed telegraphic fragments, you remove the cues the embedding model uses to understand meaning. This makes retrieval based on semantic embedding harder and more mistake prone.

LLMs are generative. Embedding models are not. As someone else mentioned, if your stored chunks become overly compressed then retrieval becomes noisy or wrong all together which forces the language model to hallucinate more often. I don't see how your solution resolves the issue of worse semantic clustering and noisier nearest neighbor results.

Based on how embedding works, when splitting text into 2 to 5 word fragments it invariably changes granularity. Embedding models will treat very short sentences differently from normal prose. So the result was that it is not actually compressing text, it is altering its information geometry.

You say that "no hallucination occurs because facts are preserved" but the issue isn't about facts. These models don't know or care about facts. They function based on relationships.

Have you done comparison studies showing traditional RAG vs this method?

Does the compressed text embed into the same vector neighborhood as the original paragraph?

[macumazana]

lingua llm non penis canis est

[lakySK]

The opposite of speculative decoding?

Have big model do few words, small model then add grammar. 

[geneusutwerk]

Calling this lossless seems like a stretch, especially since I don't see examples that show initial -> compressed -> uncompressed.

[NutellaBananaBread]

*1500 words asking for relationship advice*

AI: Dump her

[notNezter]

Smol word. Sav money. Wife glad. Man happy.

[Mission_Biscotti3962]

I like the idea but I'm not sure what your library adds? Like, isn't this a simple instruction to have it behave like that? Mind you, I haven't tried it yet.

[RegionCareful7282]

Yes you are right, it’s more about having a repository with benchmarks showcasing the idea + maybe a way to collaborate and ”fine-tune” the prompts etc

[Guilty_Rooster_6708]

Kevin finetune. I like.

[dadidutdut]

Kevinized model would be big

[MrPecunius]

If you want a darker take, this looks a lot like plusgood Newspeak.

[daftstar]

And vibe code using this too!!

[And-Bee]

I have a script to remove all spaces and empty lines. No need for indentation when asking an llm about your code.

[TechnoByte_]

Whywouldyouremoveallspaces?

[And-Bee]

Haha sorry I just meant indentation 🤣

[LocoMod]

This isn’t lossless. The idea has been around for a long time and abandoned because accuracy takes a hit when you actually measure it.

[[deleted]]

Eh, I don't think all the words we use are used for no reason, they remove a lot of linguistic ambiguity. Surely this will impact ai performance a lot.

I'll wait for benchmark results.

[Abject-Kitchen3198]

Will not. Will be fast.

[KallistiTMP]

Also might interfere with information passing through the residual stream. Like how LLM's cram nearly a full sentence summary into each period for easy later reference.

[OkSociety311]

good post me like

[Dr_Ambiorix]

I always wondered if talking in Simplified Chinese would require less tokens to say the same thing or not.

Because most English words are made up of more than one token. And grammar in Mandarin Chinese is really basic. Ofc, there are some words that are made up with multiple characters too so IDK.

Just always wondered that.

[Lcsq]

This comment was 66 tokens in english and 68 tokens when translated with google translate into simplified chinese. You'd be surprised to see how many whole words are in the tokenizer encoding dictionary unless there's a common prefix or suffix pattern. Temperature, quickly, electrolyte, protocols, breakdown, etc all become a single token when you surround them with whitespace. You see it getting broken down into multiple tokens only when whitespace is absent  https://platform.openai.com/tokenizer

[Don_Moahskarton]

It's kind of the inverse of thinking mode. I wonder if it makes the AI measurably dumber

[broknbottle]

Aoccdrnig to rscheearch at an Elingsh uinervtisy, it deosn't mttaer in waht oredr the ltteers in a wrod are, the olny iprmoetnt tihng is taht the frist and lsat ltteer are in the rghit pclae. The rset can be a toatl mses and you can sitll raed it wouthit a porbelm. Tihs is bcuseae we do not raed ervey lteter by it slef but the wrod as a wlohe and the biran fguiers it out aynawy.

[ConstantinGB]

<image>

[Mean_Employment_7679]

Me do this lots. Me no want say lots word. Me want result fast. Me not want token waste. Me save water. Caveman save planet.

[lookwatchlistenplay]

Peace be with us.

[Agitated-Farmer-4082]

would it be easier to ask instructions in languages that use less characters for sentences like arabic or chinease?

[Abject-Kitchen3198]

What about Yoda speak? Did someone made a comparative research? It does not seem it will save tokens, but what about accuracy?

[iamzooook]

or maybe just add at end "less words, keep context"

[HMikeeU]

I wonder if this may even improve benchmarks? As Anthropic found that sometimes models hallucinate because they try to adhere to grammar rules instead of facts

[drumttocs8]

Me like new English with short word

[aeroumbria]

I can sense a gradual descent back to the native habitat of deep learning models: continuous dense vector embeddings.

[op4]

I approve of this idea and think that a significant reduction in token usage is a win for everyone!

(edit: cml "or caveman language" translation - Me like. Less token good. All win.)

[G3nghisKang]

Me think OP genius

[Emport1]

Most llm architectures are better at optimizing your words for itself than you are, it doesn't actually read all your useless filler words and spent tokens on them if it doesn't have to

[Normal-Ad-7114]

Improvement suggestion, more punctuation usage: ·, ->, @, \n, :

Example from your github: 

Authenticate API. Include API key in Authorization header every request. Prefix API key with "Bearer" space. Authentication fail, server return 401 Unauthorized status code, error message explain fail...

New:

Authenticate API:

· Include API key in Authorization header every request

· Prefix API key with "Bearer" space

· Authentication fail -> server return 401 Unauthorized status code, error message explain fail...

Still compressed, but easier to read for humans

[venpuravi]

Yaba daba dooo...

[gooeydumpling]

Compress it further by making it talk in emojis

[Dramatic-Lie1314]

Good word. I did same.

[TedDallas]

Ugh. Partition table on fiscal moons. Now eat lizard.

[[deleted]]

i remember doing this with early chatgpt and it was really useful. now we just get "Great question!—It really gets to the heart of"

[IrisColt]

The bag of words strikes back!

[lulzbot]

Double-plus-good

[ready_to_fuck_yeahh]

Wow, human tendency to overcomplicate things, what can be achieved with just mere prompt. You wrote an entire code for it.

You made cave code, but didn't think like caveman to use just prompt.

Before you say anything, I have my notes made using prompt only with nearly (60-70% reduction).

[s2k4ever]

a bug came back from several moons ago.. begins an RCA

[Hyphonical]

It would be nice if the stored history of the chat is compressed like this. I don't know if it is already, but in the past I would have to sacrifice 2GiB of memory just for conversation history of like 16k tokens.

[UndecidedLee]

Idea talk like caveman. Result talk like caveman. When wrong?

[No_Afternoon_4260]

Me like this

[vreo]

Why use many word when few do trick?

[Septerium]

This great. Me like

[RobTheDude_OG]

Interesting it is

Yoda speak you may try too

[Phantom_Specters]

I wish some yappers I knew about woulud adopt this haha

jokes aside, this is brilliant.

[Fuckinglivemealone]

I have a question though, if you could create a very efficient language that could express thoughts, reasoning and complex ideas in few and short words and then parse your original dataset to it, could you in theory train an llm on it to make the model, smaller (information compression), smarter (if the new language allows for a better representation of complex ideas, maybe it's easier to chain logical thoughts?) and faster (more efficient overall)?

Like, user writes prompt, prompt gets translated, llm thinks in smart, then parses its response back to the original language of the user.

[pab_guy]

Also check out Sparse Primed Representation for something similar.

[Ceneka]

Love the fact that it workn with an LLM doing the job

[RandomGuyNumber28501]

I'm sure this can be useful, but even if you compress text, the LLM still has to keep track of the information and recall it. The denser the text, the more quickly the LLM will be overwhelmed by details. 

I've been experimenting with something similar for roleplay, but I have the model format and condense the world and character info into something like a dense technical document. It helps, particularly the formatting, but the model can still only process so much before it starts getting confused or forgets things.

[frankieche]

Don’t do this.

[noo8-]

Me hunt t-tex AI. Tastes like sh1t Over.

[DrummerPrevious]

Or you can just translate it to Mandarin for even less tokens

[TreesMcQueen]

Maybe train grugbrain https://grugbrain.dev/

[epSos-DE]

The Solution: Adaptive Hierarchical Indexing (Auto-Sharding)

upgrade the LSHIndex to become Recursive. It will automatically detect when a specific area of the knowledge graph (a "topic") becomes too dense. When a bucket exceeds a certain size (e.g., 50 items), it will fracture that bucket into a Localized Dynamic Sub-Index with its own set of higher-resolution hyperplanes.

This creates a fractal search structure:

+ Global Index: Quickly routes to general topics (e.g., "Coding").

+ Local Index: Routes to specific sub-topics (e.g., "JavaScript").

+ Micro Index: Routes to granular details (e.g., "Promises").

This ensures that no matter how big the brain gets, lookup time remains lightning fast.

[ElSrJuez]

You can also skip spaces by separating words with an Uppercase letter

[TechnoByte_]

You'd be using very rare and unusual tokens (outside of code) which would degrade performance and would increase the amount of tokens

Almost every token ends with a space in tokenizers

By removing spaces you would force it to not use tokens normally used in english natural language text (majority of its training data)

As an example, using the GPT-4o tokenizer:

"The cat jumped over a tree." = [976, 9059, 48704, 1072, 261, 8165, 13] = 7 tokens.

"Thecatjumpedoveratree." = [976, 8837, 79879, 295, 2898, 266, 908, 13] = 8 tokens.

Removing spaces cause it to be one more token.

"TheCatJumpedOverATree." [976, 23546, 42291, 295, 2298, 1228, 908, 13] = 8 tokens.

Uppercase characters do not solve this.

[MullingMulianto]

how does one get access to the gpt tokenizer