Compressed Vocabulary Expansion (CoVE)

Updated 8 July 2025

Compressed Vocabulary Expansion (CoVE) is a suite of methods that efficiently manage vocabulary size in NLP systems through compression, quantization, and dynamic tokenization.
Techniques such as adaptive level encoding, multi-codebook quantization, and vocabulary curriculum learning balance model expressiveness with limited memory and computational resources.
CoVE methodologies drive advances in domains like NLP, speech recognition, and recommender systems by enabling scalable, efficient, and adaptable language models.

Compressed Vocabulary Expansion (CoVE) is a set of methodologies and system designs aimed at enabling large-scale LLMs and related systems to efficiently increase the size and expressiveness of their vocabularies—often under tight memory, performance, or resource constraints. CoVE techniques encompass both the compression of embedding representations and strategic vocabulary expansion so that models can handle new tokens, domains, or items without incurring prohibitive storage and computation costs. These innovations play a critical role in NLP, speech recognition, information retrieval, incremental rule learning, and modern recommender systems, and have recently become central in both academic and industrial AI practice.

1. Underlying Principles and Motivations

The principal motivation for Compressed Vocabulary Expansion arises from the need to support ever-larger vocabularies or item sets in neural models while maintaining tractable resource use. Key challenges include:

The memory and inference burden of storing and updating large embedding matrices as vocabulary grows (especially in LLMs, retrieval, and recommender systems).
Preserving the semantic expressiveness and performance of models when compressing or expanding the vocabulary.
Adapting to dynamic or domain-specific data where the optimal vocabulary is not known a priori or must evolve over time.
Ensuring that models remain interpretable or explainable, which is especially pertinent in rule-based or symbolic systems.

CoVE approaches address these challenges by compressing embedding representations, dynamically selecting or merging tokens, or leveraging efficient expansion strategies that minimize additional computational overhead.

2. Compression and Quantization of Embeddings

A foundational CoVE technique is embedding compression via quantization and sparsification. Notably, the approach introduced adaptive level encoding using Lloyd’s algorithm, which quantizes each embedding dimension into a small set of discrete levels (e.g., 8 per dimension), minimizing the squared error:

$\min_{\{e_q\}} \sum_{e \in E} (e - e_q)^2$

This allows each 32-bit component in an embedding to be represented with only a few bits (e.g., 3 bits for 8 levels), reducing memory consumption by up to 10× with minimal impact on performance on word-analogy and similarity tasks (Andrews, 2015). Binary factorization strategies further sparsify embeddings, retaining only the most salient elements, thus supporting interpretability and efficiency.

Other strategies include multi-codebook quantization (Shu et al., 2017), where each word is represented as a sum of basis vectors indexed by a short, learned code. This enables storage savings of up to 98% with lossless performance on NLP benchmarks. Modern vocabulary transfer (Gee et al., 15 Feb 2024) similarly uses token partitioning and averaging to efficiently initialize compressed, domain-specific embeddings.

3. Dynamic and Iterative Vocabulary Expansion

Rather than using a static vocabulary, CoVE methods increasingly employ dynamic vocabularies tailored to input data, task, or learning progress:

Vocabulary Curriculum Learning: Alternates between model optimization and vocabulary expansion using entropy-based criteria, forming new tokens only when sequences are sufficiently predictable. This mimics adaptive, hierarchical acquisition and yields improved pretraining efficiency and representation granularity (Yu, 25 Feb 2025).
zip2zip Framework: Applies streaming LZW compression at inference, incrementally merging frequent token sequences into hypertokens and computing embeddings dynamically via a hyper-encoder. This results in a dynamic, context-dependent vocabulary that adjusts on the fly (Geng et al., 1 Jun 2025).
Corpus-Specific and Domain-Adaptive Vocabularies: Tokenizers trained directly on the target corpus or domain provide more natural token coverage, optimizing both compression and downstream performance in settings such as retrieval and specialized business applications (Yu et al., 12 Jan 2024, Gee et al., 15 Feb 2024).

In all such approaches, the vocabulary adapts during pretraining, fine-tuning, or inference, seeking a balance between expressiveness and computational tractability.

4. Efficient Embedding Initialization and Transfer

A critical aspect of CoVE is ensuring that new tokens—added during expansion or compression—have effective, semantically faithful embedding representations:

Convex Combination Initialization: Theoretical results (Mundra et al., 8 Jul 2024) demonstrate that initializing new embeddings as convex combinations of existing ones (ensuring they lie inside the convex hull) preserves the model’s original behavior and prediction dynamics.
Heuristic and Alignment-Based Methods: Token embeddings may be initialized via averaging over segmented source tokens (mean), leveraging merge rules in the tokenizer, or using token alignment frequency statistics (Yamaguchi et al., 17 Jun 2024). These heuristic strategies are robust and effective even when adaptation data is scarce.
Compositional Code Learning: Discrete codebooks (codes learned via Gumbel-softmax) select basis vectors for reconstruction, facilitating compact representation and efficient addition of new tokens (Shu et al., 2017).

All methods seek to minimize information loss or disruption to the pretrained model while supporting vocabulary adaptation.

5. Application Domains

CoVE is implemented across a wide range of domains:

LLMs: Expanding or compressing the tokenizer and vocabularies for efficient multilingual adaptation, low-resource language support, and domain transfer, all while maintaining or improving inference speed and accuracy (Mundra et al., 8 Jul 2024, Yamaguchi et al., 17 Jun 2024).
Recommender Systems: Each item in the catalog is given a unique token in the expanded vocabulary. Hashing-based embedding compression techniques ensure that, even with millions of items, memory and computation remain scalable, and inference proceeds via direct ID prediction (Zhang et al., 24 Jun 2025).
Information Retrieval: Corpus-specific tokenizers and vocabulary expansion improve retrieval precision and system efficiency, with easily tunable trade-offs between latency and quality (Yu et al., 12 Jan 2024).
Speech Recognition: Expanded vocabularies (through lexicon or graph modifications) allow recognition of OOV words without retraining expensive acoustic models (Khassanov et al., 2018, Malkovsky et al., 2020).
Rule-Based Symbolic Models: Iterative vocabulary expansion strategies coupled with confidence-based filtering reduce system memory footprint while maintaining or raising rule reliability for tasks such as insurance claims processing (Nössig et al., 30 Oct 2024).

6. Scaling, Efficiency, and Trade-Offs

A central theme of CoVE is the explicit management of trade-offs between model expressiveness, performance, memory footprint, and compute requirements:

Scalability: Hashing, quantization, and grouping approaches permit vocabulary or embedding tables to be increased by orders of magnitude while controlling resource use (Andrews, 2015, Zhang et al., 24 Jun 2025, Vennam et al., 10 Nov 2024).
Inference Speed and Throughput: Sequence compression (as with zip2zip and LLM head grouping) directly reduces the number of computation steps and memory bandwidth, enabling up to 3x faster processing and enabling deployment in low-compute or real-time environments (Vennam et al., 10 Nov 2024, Geng et al., 1 Jun 2025).
Quality–Efficiency Trade-offs: Whether by varying vocabulary size, the number of expansion tokens kept, degree of sparsification, or compression parameters, practitioners can finely tune system behavior to the needs of specific tasks or deployments (Yu et al., 12 Jan 2024, Gee et al., 15 Feb 2024).

The success of CoVE frameworks depends on judicious configuration of these factors, often requiring domain- or application-specific calibration.

7. Future Directions

Emerging research accelerates the evolution of CoVE methodologies:

Extension to even larger models and broader domains, including multimodal or non-textual data (Yu, 25 Feb 2025).
Development of more sophisticated embedding compression techniques (quantization, low-rank, or adaptive schemes) to push memory and compute constraints further (Zhang et al., 24 Jun 2025).
Investigation of fully dynamic tokenization and token curriculum learning at massive scale, mirroring human-like language acquisition processes (Yu, 25 Feb 2025).
Integration of synthetic data for adaptation and compression in extremely low-resource or zero-shot settings (Yamaguchi et al., 17 Jun 2024).

Continued exploration is likely to yield further gains in pretraining efficiency, cross-lingual adaptation, and practical scalability of large-scale models deploying compressed and/or adaptively expanded vocabularies.

CoVE constitutes a unifying perspective on vocabulary management in NLP and related AI domains, combining quantization, efficient expansion, dynamic adaptation, and application-specific embedding management to address the pressing needs of modern, scalable, and versatile AI systems.