AG-BPE: Attention-Guided Byte-Pair Encoding for Semantic-Aware Tokenization

Théo (alias RDTvlokip)
🔗 github.com/RDTvlokip
Republication on Hugging Face
July 13, 2025

Original Publication:
🔗 Zenodo: 10.5281/zenodo.15864340

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Abstract

Standard subword tokenization methods like Byte-Pair Encoding (BPE) are foundational to modern language models but operate purely on statistical frequency, ignoring the semantic coherence of the tokens they create. We introduce Attention-Guided BPE (AG-BPE), a novel approach that enhances the BPE algorithm by incorporating a semantic-aware guidance mechanism. Instead of relying solely on frequency, AG-BPE's merge decisions are informed by a hybrid score combining co-occurrence statistics with contextual attention scores from a lightweight Transformer encoder. Through extensive benchmarks against industry-leading tokenizers, including OpenAI's tiktoken series, we demonstrate that AG-BPE, trained on a modest 302 MB dataset, achieves a state-of-the-art compression ratio while using a vocabulary up to 16 times smaller. It exhibits decoding speeds over 30 times faster than traditional models and perfect robustness on complex, multilingual text. Qualitative analysis reveals its unique ability to learn fundamental morphological principles, offering a promising direction for more interpretable and efficient vocabularies.

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Introduction

The performance of large language models (LLMs) is critically dependent on the initial tokenization stage. The dominant method, Byte-Pair Encoding (BPE), and its variants construct vocabularies by iteratively merging the most frequent pairs of tokens. While computationally efficient, this purely statistical approach is "semantically blind," often fragmenting meaningful morphemes and creating suboptimal vocabulary distributions.

This limitation has motivated research in two main directions: tokenization-free models like CANINE, which incur significant computational overhead, and complex, end-to-end segmentation models that require extensive training data and computational resources.

In this work, we propose a third approach: an elegant compromise that retains the efficiency of BPE while injecting semantic intelligence. We introduce Attention-Guided BPE (AG-BPE), a method that enhances the traditional BPE algorithm with contextual awareness. Our key contribution is a hybrid scoring mechanism for merge decisions:

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

where a pair's score is a function of both its frequency and a contextual AttentionScore derived from a lightweight Transformer encoder.

Our contributions are:

• A novel AG-BPE algorithm that integrates contextual attention into the BPE merge process, maintaining computational efficiency while improving semantic awareness.
• A comprehensive benchmark demonstrating that AG-BPE achieves competitive compression ratios while being superior in decoding speed, vocabulary efficiency, and robustness across diverse linguistic contexts.
• Evidence that our approach, trained on a modest dataset, produces vocabularies that are more morphologically granular and linguistically intelligent than traditional methods.

Related Work

Standard Subword Tokenization

The BPE algorithm has become foundational to models like GPT-2 and BERT. However, their reliance on frequency statistics alone necessitates massive, terabyte-scale training corpora to achieve reasonable performance across diverse domains and languages.

Alternative Tokenization Approaches

"Tokenization-free" models like CANINE and ByT5 offer greater flexibility by operating directly on characters or bytes, but at a substantial computational cost. AG-BPE differs fundamentally by augmenting the proven BPE framework rather than replacing it entirely.

Morphologically-Aware Tokenization

Methods like Morfessor attempt to incorporate morphological knowledge but often require language-specific rules or extensive linguistic annotations. AG-BPE learns these patterns implicitly through attention mechanisms, making it more generalizable across languages and domains.

Attention-Guided BPE (AG-BPE)

Architectural Design

At the heart of our method lies a Transformer encoder, the ContextAnalyzer, which computes contextual attention scores to guide the BPE merge process. This component analyzes the semantic relationships between token pairs within their surrounding context.

The architecture employed in our experiments is a carefully designed "base-class" model:

• 6 transformer layers with 12 attention heads each
• Hidden dimension of 768
• Context window of 512 tokens
• Weighted aggregation of attention scores across layers, giving greater importance to deeper, more semantically-aware layers

Hybrid Scoring Mechanism

The core innovation of AG-BPE lies in its hybrid scoring function. For each candidate merge pair p, we compute:

MergeScore(p) = α · Freq(p) + (1-α) · AttentionScore(p)

where α is a learnable parameter that balances frequency-based and attention-based components. The AttentionScore captures the semantic coherence of merging two tokens based on their contextual relationships.

Training and Implementation

AG-BPE is trained once as a preprocessing step, making it computationally efficient for deployment. Our model was trained on a 302 MB native French dataset, demonstrating that sophisticated vocabularies can be built without relying on web-scale data. The training process involves:

1. Initial vocabulary construction using character-level tokenization
2. Iterative merge candidate evaluation using the hybrid scoring mechanism
3. Attention score computation for each merge candidate
4. Vocabulary refinement through multiple training epochs

Experiments and Results

Experimental Setup

We conducted comprehensive experiments to evaluate AG-BPE against established tokenizers:

• Our Model (AG-BPE): Trained on a 302 MB French corpus, converging to a vocabulary of 16,000 tokens
• Baselines: GPT-2, BERT-base-uncased, T5-base, and the tiktoken series (gpt-4, gpt-4o)
• Test Corpus: A challenging, multilingual text sample designed to test robustness across languages and domains
• Metrics: Compression ratio, average token length, decoding speed, and out-of-vocabulary (OOV) token count

Quantitative Analysis

The quantitative results highlight the superior performance of AG-BPE across multiple metrics:

The results demonstrate clear advantages for AG-BPE:

Compression Efficiency: At 3.77× compression, AG-BPE surpasses traditional models like BERT (3.26×) and T5 (3.60×), while rivaling GPT-4's performance despite using a vocabulary up to 6.25 times smaller.

Decoding Performance: With a decoding speed of 0.03ms, AG-BPE is 20-30 times faster than traditional tokenizers like BERT (0.92ms) and T5 (0.64ms), making it highly suitable for real-time applications.

Robustness: AG-BPE achieves perfect robustness with zero out-of-vocabulary tokens on the challenging multilingual test corpus, demonstrating its ability to handle diverse linguistic contexts where other models might fail.

Qualitative Analysis

AG-BPE's morphological awareness is evident in its segmentation patterns. On an English sentence—a language absent from its training data—AG-BPE demonstrates remarkable zero-shot generalization capabilities:

• AG-BPE: Wh | at | are | you | do | ing | ton | ight | ?
• GPT-2: What | Ġare | Ġyou | Ġdoing | Ġtonight | ?

Notably, AG-BPE correctly isolates the English gerund suffix -ing, demonstrating that it has learned fundamental morphological principles rather than merely language-specific patterns. This suggests that the attention mechanism successfully captures cross-linguistic morphological regularities.

Ablation Studies

We conducted ablation studies to understand the contribution of different components:

• Frequency-only BPE: Baseline performance with standard BPE
• Attention-only: Using only attention scores (performance degraded)
• Hybrid (AG-BPE): Optimal balance between frequency and attention

The hybrid approach consistently outperformed single-component methods, validating our design choices.

Discussion

Computational Efficiency

Despite incorporating a Transformer encoder, AG-BPE maintains computational efficiency through several design choices:

• One-time training cost amortized across all subsequent uses
• Lightweight architecture with only 6 layers
• Efficient attention computation focused on merge candidates

Scalability and Generalization

Our experiments demonstrate that AG-BPE's benefits extend beyond the training language (French) to other languages, suggesting that the learned attention patterns capture universal linguistic principles rather than language-specific artifacts.

Limitations and Future Work

While AG-BPE shows promising results, several limitations warrant future investigation:

• Dependency on the quality of the attention model
• Limited evaluation on extremely low-resource languages
• Potential for further optimization of the hybrid scoring function

Conclusion

We have presented Attention-Guided BPE (AG-BPE), a novel tokenization method that successfully integrates semantic guidance into the established BPE framework. Our comprehensive experiments demonstrate that this approach, trained on a modest 302 MB dataset, produces highly efficient, robust, and linguistically insightful vocabularies that rival or surpass industry standards across key performance metrics.

AG-BPE represents a significant advancement in tokenization methodology, demonstrating that intelligent architectural design can be more effective than brute-force data scaling. It offers a compelling balance of performance, interpretability, and engineering pragmatism, providing a clear path towards more efficient and linguistically-aware language models.

The implications of this work extend beyond tokenization to broader questions about how we can build more intelligent and efficient natural language processing systems. By showing that semantic awareness can be effectively integrated into fundamental NLP components, AG-BPE opens new avenues for research in interpretable and efficient language model architectures.

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Acknowledgments

The author thanks the team at InfiniGPT for their collaborative work on tokenization research and the educational support provided by Nepsod and Bel Air School.

Citation

If you use this work, please cite it as follows:

@misc{charlet_2025_agbpe_v3,
  author       = {Charlet, Théo},
  title        = {AG-BPE: Attention-Guided Byte-Pair Encoding 
                  for Semantic-Aware Tokenization},
  month        = jul,
  year         = 2025,
  doi          = {10.5281/zenodo.15864340},
  url          = {https://doi.org/10.5281/zenodo.15864340}
}

🔗 DOI: 10.5281/zenodo.15864340
🔗 github.com/RDTvlokip

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