Architectural Evaluation of Subword Tokenization and Compact Language Models (CLMs) for Resource-Constrained NLP Deployment


Authors : Arnab Sen

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/582w69uh

Scribd : https://tinyurl.com/mr495sb3

DOI : https://doi.org/10.38124/ijisrt/25nov578

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Abstract : Background The advancement of Natural Language Processing (NLP) is constrained by a fundamental dilemma: the immense resource requirements of Large Language Models (LLMs) versus the demand for efficient, high-performance deployment in resource- limited settings, such as edge computing.1 This work establishes a necessary comparison between efficient deep learning alternatives and classical statistical methods.1  Materials and Methods A structural and performance analysis is conducted, comparing two distinct model classes: traditional statistical N-gram models and modern Transformer-based Compact Language Models (CLMs).1 The methodology critically evaluates core architectural differences, efficiency metrics, and the transformative impact of tokenization strategies. Key quantitative metrics, including Perplexity (PPL), and qualitative measures, such as semantic coherence and visual embedding consistency (via t-SNE), are employed.1  Results CLMs, achieved through rigorous optimization techniques like pruning and quantization, exhibit superior representational capacity and drastically faster development cycles compared to resource-intensive LLMs.1 N-gram models are fundamentally hindered by the exponential challenge of data sparsity and the inability to capture context beyond a fixed, narrow window.1 Crucially, the CLM's implementation of subword tokenization (specifically Byte Pair Encoding, BPE) structurally solves the Out-of-Vocabulary (OOV) problem, preserving semantic information that N-gram models invariably destroy by collapsing unseen words into a generic $\langle \text{unk} \rangle$ token.1  Conclusion The architectural stability, efficiency, and deep contextual fidelity afforded by optimized Compact Language Models position them as the definitive, operationally feasible choice for high-accuracy, specialized NLP tasks at the network edge.1 While N-gram models may serve as simple baselines for modeling localized statistical distributions, their severe architectural limitations make them unsuitable for modern applications requiring complex semantic understanding.1

Keywords : Compact Language Models (CLMs); Subword Encoding; Byte Pair Encoding (BPE); Edge Computing; Perplexity; Transformer.

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  1. Architectural Evaluation of Subword Tokenization and Compact Language Models (CLMs) for Resource-Constrained NLP Deployment.docx

Background The advancement of Natural Language Processing (NLP) is constrained by a fundamental dilemma: the immense resource requirements of Large Language Models (LLMs) versus the demand for efficient, high-performance deployment in resource- limited settings, such as edge computing.1 This work establishes a necessary comparison between efficient deep learning alternatives and classical statistical methods.1  Materials and Methods A structural and performance analysis is conducted, comparing two distinct model classes: traditional statistical N-gram models and modern Transformer-based Compact Language Models (CLMs).1 The methodology critically evaluates core architectural differences, efficiency metrics, and the transformative impact of tokenization strategies. Key quantitative metrics, including Perplexity (PPL), and qualitative measures, such as semantic coherence and visual embedding consistency (via t-SNE), are employed.1  Results CLMs, achieved through rigorous optimization techniques like pruning and quantization, exhibit superior representational capacity and drastically faster development cycles compared to resource-intensive LLMs.1 N-gram models are fundamentally hindered by the exponential challenge of data sparsity and the inability to capture context beyond a fixed, narrow window.1 Crucially, the CLM's implementation of subword tokenization (specifically Byte Pair Encoding, BPE) structurally solves the Out-of-Vocabulary (OOV) problem, preserving semantic information that N-gram models invariably destroy by collapsing unseen words into a generic $\langle \text{unk} \rangle$ token.1  Conclusion The architectural stability, efficiency, and deep contextual fidelity afforded by optimized Compact Language Models position them as the definitive, operationally feasible choice for high-accuracy, specialized NLP tasks at the network edge.1 While N-gram models may serve as simple baselines for modeling localized statistical distributions, their severe architectural limitations make them unsuitable for modern applications requiring complex semantic understanding.1

Keywords : Compact Language Models (CLMs); Subword Encoding; Byte Pair Encoding (BPE); Edge Computing; Perplexity; Transformer.

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