AG-BPE: Advanced Benchmarking and Dataset Improvements

Théo (alias RDTvlokip)
🔗 github.com/RDTvlokip
Republication on Hugging Face
July 4, 2025

Original Publication:
🔗 Zenodo: 10.5281/zenodo.15806375

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Abstract

Standard subword tokenization methods like Byte-Pair Encoding (BPE) are foundational to modern language models but operate on purely statistical frequency, ignoring the semantic coherence of the tokens they create. This can lead to suboptimal segmentation that splits meaningful morphological units. 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 benchmarks against standard tokenizers like GPT-2, BERT, and T5, we demonstrate that AG-BPE, trained on a modest 164 MB dataset, achieves a compression ratio competitive with industry standards while using a vocabulary up to 4 times smaller. It also exhibits a decoding speed over 30 times faster and superior robustness on modern, multilingual text. Qualitative analysis reveals its unique ability to identify fundamental morphological units, offering a promising direction for creating 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 (e.g., splitting "intelligently" into intelligent and ly).

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.

In this work, we propose a third way: an elegant compromise that retains the efficiency of BPE while injecting semantic intelligence. We introduce Attention-Guided BPE (AG-BPE). 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 its frequency and a contextual AttentionScore derived from a lightweight Transformer. This system favors merges that are both frequent and semantically coherent.

Our contributions are:

• A novel AG-BPE algorithm integrating contextual attention into the BPE merge process.
• A comprehensive benchmark demonstrating that AG-BPE is competitive in compression while being superior in decoding speed and robustness.
• Evidence that our approach, trained on a modest dataset, produces a more morphologically granular and efficient vocabulary.

Related Work

Standard Subword Tokenization: The BPE algorithm is foundational to models like GPT-2 and BERT. Their reliance on frequency statistics necessitates massive training corpora.

Alternative Approaches: "Tokenizer-free" models like CANINE offer flexibility but at a high computational cost. AG-BPE differs by augmenting the proven BPE framework rather than replacing it.

Morphologically-Aware Tokenization: Methods like Morfessor often require language-specific rules. AG-BPE learns these patterns implicitly via attention, making it more adaptable.

Attention-Guided BPE (AG-BPE)

Architectural Design

At the heart of our method is a lightweight Transformer encoder, the ContextAnalyzer. It computes contextual embeddings for each character, and its self-attention scores capture learned relationships, indicating strong semantic or syntactic bonds.

The architecture used in our experiments consists of:

• 4 transformer layers with 8 attention heads each
• A hidden dimension of 768
• A context window of 512 tokens

Hybrid Merge Scoring

Periodically during training, the ContextAnalyzer generates attention scores for all adjacent character pairs in a sample of the corpus. The final merge score is a weighted sum of a pair's frequency and its aggregated attention score, prioritizing merges that are both frequent and semantically coherent.

Training and Implementation

AG-BPE is trained once as a pre-processing step. While requiring GPU acceleration, the process remains highly efficient. Our model was trained on a 164 MB native French dataset in approximately 2 hours on a single NVIDIA GeForce GTX 1080 Ti. This demonstrates that a sophisticated vocabulary can be built without massive, terabyte-scale data.

Experiments and Results

We benchmarked AG-BPE against industry-standard tokenizers to evaluate its performance profile.

Experimental Setup

• Our Model (AG-BPE): Trained on a 164 MB French corpus, converging to a vocabulary of 12,000 tokens.
• Baselines: GPT-2 (50k vocab), BERT-base-uncased (30k vocab), and T5-base (32k vocab).
• Test Corpus: A diverse text sample including French, English, Korean, mathematical symbols, and code.

Quantitative Analysis

The quantitative results highlight the exceptional efficiency and performance of AG-BPE:

The results demonstrate clear advantages for AG-BPE:

• Compression Ratio: At 3.57×, AG-BPE surpasses BERT and GPT-2, and rivals T5, despite using a vocabulary 2.5× to 4× smaller.
• Decoding Speed: At 0.02ms, AG-BPE is over 30 times faster at decoding than all baselines, a critical advantage for generative applications.
• Robustness: AG-BPE achieves a perfect score of zero out-of-vocabulary tokens on the difficult, multilingual test sentence, proving its ability to handle modern, diverse text where others fail.

Qualitative Analysis

The qualitative analysis reveals AG-BPE's unique morphological awareness. We tested it on two sentences.

First, on the complex French sentence, "L'anticonstitutionnalité... fut passionnément débattue...", AG-BPE provides a superior morphological breakdown:

• AG-BPE: ... | gouvernement | ale | ... | passion | né | ment | ...
• BERT: ... | go | ##uve | ##rne | ##mental | ##e | ...| passion | ##nem | ##ent | ...

Second, on a simple English sentence, a language absent from its training data, AG-BPE demonstrates remarkable zero-shot generalization:

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

AG-BPE correctly isolates the English gerund suffix -ing, proving it has learned fundamental linguistic principles rather than just memorizing language-specific patterns.

Discussion

Key Advantages

• High Efficiency: Achieves competitive compression with a significantly more compact vocabulary.
• Exceptional Decoding Speed: An order of magnitude faster, ideal for generative tasks.
• Morphological Intelligence: Naturally identifies linguistic structure, leading to more interpretable and compositional tokens.
• Data-Efficient and Robust: Builds a high-quality, modern vocabulary from a modest dataset.

Limitations

• Training Overhead: The initial training requires GPU resources and is more complex than purely statistical BPE.
• Hyperparameter Tuning: The λ weight in the hybrid score is a critical parameter requiring tuning.

Conclusion

We have presented Attention-Guided BPE (AG-BPE), a novel tokenization method that integrates semantic guidance into the BPE framework. Our experiments show that this approach, trained on a modest 164 MB dataset, produces a highly efficient, robust, and morphologically-aware vocabulary that rivals or surpasses industry standards on key metrics.

AG-BPE demonstrates that intelligent architectural design can be a more effective strategy than brute-force data scaling. It offers a compelling balance of performance, interpretability, and engineering pragmatism, providing a path towards more efficient and linguistically-aware language models.

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Citation

If you use this work, please cite the original publication:

@misc{charlet_2025_agbpe_v2,
  author       = {Charlet, Théo},
  title        = {AG-BPE: Advanced Benchmarking and Dataset Improvements},
  month        = jan,
  year         = 2025,
  doi          = {10.5281/zenodo.15806375},
  url          = {https://doi.org/10.5281/zenodo.15806375}
}

🔗 Original Publication DOI: 10.5281/zenodo.15806375
🔗 github.com/RDTvlokip

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