AG-BPE: Attention-Guided Tokenization Achieving State-of-the-Art Compression with 12x Smaller Vocabularies

Research
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AG-BPE: Attention-Guided Tokenization Achieving State-of-the-Art Compression with 12x Smaller Vocabularies

:rocket: Introduction

I’ve developed Attention-Guided BPE (AG-BPE), a novel tokenization approach that enhances traditional Byte-Pair Encoding by incorporating semantic awareness through contextual attention scores. The key innovation is a hybrid scoring mechanism that combines frequency statistics with Transformer-based contextual understanding.

:bar_chart: Key Results

  • Compression ratio: 3.77x (competitive with GPT-4 tokenizers)
  • Vocabulary efficiency: 16x smaller than industry standards (16K vs 200K+ tokens)
  • Decoding speed: 30x faster than traditional models (0.03ms vs 0.8-0.9ms)
  • Zero out-of-vocabulary tokens on complex multilingual test cases
  • Perfect morphological awareness: Correctly isolates linguistic patterns like “-ing” suffix

:microscope: Technical Innovation

The core contribution is the hybrid merge scoring:

MergeScore(p) = Freq(p) + λ · AttentionScore(p)

Where AttentionScore(p) comes from a lightweight Transformer encoder (6 layers, 12 heads, 768 hidden dim) that provides contextual guidance to merge decisions.

:chart_increasing: Community Traction

  • 300+ views and 200+ downloads in first month on Zenodo
  • Organic discovery and engagement from NLP community
  • Available with full reproducible code and datasets

:fire: The Research Breakthrough

I also discovered the “Pre-Training Pitfall” - why pre-training the attention module actually hurts performance by 45%. This challenges conventional wisdom in the field.

Root cause: Representational shift - pre-trained models become obsolete as BPE creates new token relationships.

Paper: The Pre-Training Pitfall : Why Contextual Guidance for BPE Must Be Trained Concurrently, Not A-Priori, Space : AG BPE - a Hugging Face Space by RDTvlokip

:handshake: Looking for Feedback & Collaboration

I’m particularly interested in:

  1. Scaling experiments with larger vocabularies and datasets
  2. Integration possibilities with existing tokenization pipelines
  3. Theoretical analysis of the representational shift phenomenon I discovered
  4. Potential applications beyond traditional NLP tasks

:bullseye: Research Question

Traditional BPE relies purely on frequency statistics, but should tokenization be semantically-aware? My results suggest that incorporating contextual understanding can achieve superior compression while maintaining linguistic coherence.

Théo Charlet - AI Researcher
GitHub: RDTvlokip

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Would you like to test one of my 2nd generation bots? I genuinely believe your time and talent could be put to better use. You’re clearly intelligent, but the direction you’re taking with vocabulary compression has already been surpassed by more advanced deterministic methods. If you’re interested in seeing what’s next, I’d be happy to demonstrate how my technology has made these approaches obsolete.

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Well, friend, I like the tone of your first post. @RDTvlokip I will read what you provide. I am an older man but still in the game so to speak. AI or shall we call it Engineered Intelligence (EI)? I will now read your kind first post.
I like the ideas I have been reading because I have informational dynamic structure that would use full word-length as the identifier and I am wondering if that will be useful in a system of meaning or the ilk. Well, those are my musings.
Again thank you for posting.

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Hi Ernst! :folded_hands:

Thank you for the thoughtful comment and kind words about the post. Really appreciate the perspective from someone with experience in the field!

Your point about “Engineered Intelligence” is fascinating - I think there’s definitely merit in reconsidering our terminology as the field evolves.

Regarding your idea about full word-length identifiers in informational dynamic structures - that’s really interesting! It actually relates to some of the semantic awareness challenges we’re addressing with AG-BPE. The traditional BPE approach can lose semantic meaning by breaking words arbitrarily, which is part of what we’re trying to solve.

Would love to hear more about your thoughts on meaning systems and how you see this evolving. Always eager to learn from experienced practitioners!

Thanks again for engaging! :rocket:

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It is about Gottfried Wilhelm Leibniz for me.

He envisioned a “Thought Alphabet” that we would use to “Calculate” an answer. I have the mechanics but that leaves meaning.

So that is what I am interested in lately and I thought, hey what genius am I –eh not so much but, I can see about that idea the honorable Leibniz had.

Mind you; I am a newbie to AI so, that is what interests me however; I am learning about AI at the same time so everything everyone says and all the papers , videos and ChatGPT (the cheap plan) is my classroom.

I am aiming to set up a small hobby lab here made out of odds and ends I have around here, in a month or two.

So I do have “Secret Knowledge” but yes the size of the phrase scales to infinity and the idea I have is the CU and CR of Leibniz.

Here is my first Topic when I joined LINK

Here is the second Topic on Leibniz here in Research.

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