A byte-level language model architecture that dynamically merges tokens for improved efficiency
Core Idea: Mr. T5 improves byte-level language modeling efficiency by using a learned gating mechanism that dynamically merges tokens after initial processing, significantly reducing sequence length while maintaining performance.
Key Elements
Architectural Foundation
- Built upon Bite T5, a byte-level variant of the T5 encoder-decoder model
- Maintains byte-level processing advantages (character awareness, language fairness)
- Addresses efficiency limitations through dynamic token merging
- Encoder-heavy design with most parameters concentrated in the encoder
Dynamic Token Merging Mechanism
- Early Layer Processing: Initial layers process full byte sequence
- Contextual information spreads through attention mechanism
- Information from multiple tokens merges into representations
- Learned Gating Mechanism: Determines which tokens to keep or drop
- Token selection based on information content
- No prior assumptions about compression rates for different languages
- Training Implementation: Implemented as attention masking
- Masked tokens prevented from influencing other tokens
- Maintained in sequence but effectively ignored
- Inference Implementation: Hard deletion for maximum efficiency
- Identified tokens completely removed from sequence
- Sequence physically shortened for computational gains
Performance Characteristics
- Compression Rates: Typically 45-50% sequence length reduction
- Controllable via hyperparameters
- Language-specific rates learned implicitly
- Efficiency Gains: 45% speed improvement on classification tasks
- Most beneficial for long encoder inputs with short decoder outputs
- Maintains equivalent performance to uncompressed model
- Benchmark Results:
- XNLI (multilingual classification): Matched or exceeded Bite T5
- TyDiQA (multilingual question answering): Comparable to Bite T5
- Character manipulation tasks: Preserved Bite T5's advantages over subword models
Language-Specific Behavior
- Learned compression rates vary by language information density
- Chinese characters compressed less due to inherent information density
- Latin script languages compressed more aggressively
- No explicit language-specific rules required
Scaling Properties
- Larger models (1.2B parameters) showed greater efficiency gains
- Technique potentially applicable to even larger models
Additional Connections
- Broader Context: Encoder-Decoder Architectures (foundation architecture)
- Applications: Inference Optimization Techniques (practical application)
- See Also: Tokenization in Language Models (the problem being addressed)
References
- Kini, J. (2024). Mr. T5: Dynamic token merging for efficient byte-level language models. TWIML AI Podcast interview.
- Kini, J., et al. (2023). Mr. T5: Dynamic token merging for efficient byte-level language models. Research paper.
#nlp #language-models #architecture #efficiency #byte-level-models
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