How Different Tokenization Algorithms Impact LLMs and Transformer Models for Binary Code Analysis

• URL: http://arxiv.org/abs/2511.03825v1
• Date: Wed, 05 Nov 2025 19:45:26 GMT
• Title: How Different Tokenization Algorithms Impact LLMs and Transformer Models for Binary Code Analysis
• Authors: Ahmed Mostafa, Raisul Arefin Nahid, Samuel Mulder,
• Abstract summary: Despite its significance, tokenization in the context of assembly code remains an underexplored area.<n>We explore preprocessing customization options and pre-tokenization rules tailored to the unique characteristics of assembly code.<n>We compare tokenizers based on tokenization efficiency, vocabulary compression, and representational fidelity for assembly code.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tokenization is fundamental in assembly code analysis, impacting intrinsic characteristics like vocabulary size, semantic coverage, and extrinsic performance in downstream tasks. Despite its significance, tokenization in the context of assembly code remains an underexplored area. This study aims to address this gap by evaluating the intrinsic properties of Natural Language Processing (NLP) tokenization models and parameter choices, such as vocabulary size. We explore preprocessing customization options and pre-tokenization rules tailored to the unique characteristics of assembly code. Additionally, we assess their impact on downstream tasks like function signature prediction -- a critical problem in binary code analysis. To this end, we conduct a thorough study on various tokenization models, systematically analyzing their efficiency in encoding assembly instructions and capturing semantic nuances. Through intrinsic evaluations, we compare tokenizers based on tokenization efficiency, vocabulary compression, and representational fidelity for assembly code. Using state-of-the-art pre-trained models such as the decoder-only Large Language Model (LLM) Llama 3.2, the encoder-only transformer BERT, and the encoder-decoder model BART, we evaluate the effectiveness of these tokenizers across multiple performance metrics. Preliminary findings indicate that tokenizer choice significantly influences downstream performance, with intrinsic metrics providing partial but incomplete predictability of extrinsic evaluation outcomes. These results reveal complex trade-offs between intrinsic tokenizer properties and their utility in practical assembly code tasks. Ultimately, this study provides valuable insights into optimizing tokenization models for low-level code analysis, contributing to the robustness and scalability of Natural Language Model (NLM)-based binary analysis workflows.

Related papers

• Compressed code: the hidden effects of quantization and distillation on programming tokens [0.0]
  Large Language Models (LLMs) have demonstrated exceptional code generation capabilities, yet their token-level mechanisms remain underexplored.<n>We introduce a novel cold-start probability analysis method that provides insights into model behavior without requiring explicit prompts.<n>We present a comprehensive evaluation of how different model optimization techniques affect token-level representations and code generation quality.
  arXiv  Detail & Related papers  (2026-01-05T21:32:47Z)
• Unveiling Decision-Making in LLMs for Text Classification : Extraction of influential and interpretable concepts with Sparse Autoencoders [0.0]
  We present a novel SAE-based architecture tailored for text classification.<n>We benchmark this architecture against established methods such as ConceptShap, Independent Component Analysis, and other SAE-based concept extraction techniques.<n>Our empirical results show that our architecture improves both the causality and interpretability of the extracted features.
  arXiv  Detail & Related papers  (2025-06-30T15:18:50Z)
• Enhancing LLM Character-Level Manipulation via Divide and Conquer [74.55804812450164]
  Large Language Models (LLMs) have demonstrated strong generalization capabilities across a wide range of natural language processing (NLP) tasks.<n>They exhibit notable weaknesses in character-level string manipulation, struggling with fundamental operations such as character deletion, insertion, and substitution.<n>We propose Character-Level Manipulation via Divide and Conquer, a novel approach designed to bridge the gap between token-level processing and character-level manipulation.
  arXiv  Detail & Related papers  (2025-02-12T07:37:39Z)
• Tokenization Standards for Linguistic Integrity: Turkish as a Benchmark [0.29687381456163997]
  Tokenization is a fundamental preprocessing step in NLP, directly impacting large language models' ability to capture syntactic, morphosyntactic, and semantic structures.<n>This paper introduces a novel framework for evaluating tokenization strategies, addressing challenges in morphologically rich and low-resource languages.
  arXiv  Detail & Related papers  (2025-02-10T21:47:49Z)
• A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer [15.689556592544667]
  We introduce ProTST, a novel transformer-based methodology for binary code embedding.<n>ProTST employs a hierarchical training process based on a unique tree-like structure.<n>Results show that ProTST yields an average validation score (F1, MRR, and Recall@1) improvement of 14.8% compared to traditional two-stage training.
  arXiv  Detail & Related papers  (2024-12-15T13:04:29Z)
• When Every Token Counts: Optimal Segmentation for Low-Resource Language Models [0.0]
  We show that an optimal Byte-Pair (BPE) configuration significantly reduces token count compared to greedy segmentation.<n>Our findings suggest that compression-optimized tokenization strategies could provide substantial advantages for multilingual and low-resource language applications.
  arXiv  Detail & Related papers  (2024-12-09T19:11:54Z)
• A Theoretical Perspective for Speculative Decoding Algorithm [60.79447486066416]
  One effective way to accelerate inference is emphSpeculative Decoding, which employs a small model to sample a sequence of draft tokens and a large model to validate. This paper tackles this gap by conceptualizing the decoding problem via markov chain abstraction and studying the key properties, emphoutput quality and inference acceleration, from a theoretical perspective.
  arXiv  Detail & Related papers  (2024-10-30T01:53:04Z)
• STAB: Speech Tokenizer Assessment Benchmark [57.45234921100835]
  Representing speech as discrete tokens provides a framework for transforming speech into a format that closely resembles text. We present STAB (Speech Tokenizer Assessment Benchmark), a systematic evaluation framework designed to assess speech tokenizers comprehensively. We evaluate the STAB metrics and correlate this with downstream task performance across a range of speech tasks and tokenizer choices.
  arXiv  Detail & Related papers  (2024-09-04T02:20:59Z)
• A Thorough Examination of Decoding Methods in the Era of LLMs [72.65956436513241]
  Decoding methods play an indispensable role in converting language models from next-token predictors into practical task solvers. This paper provides a comprehensive and multifaceted analysis of various decoding methods within the context of large language models. Our findings reveal that decoding method performance is notably task-dependent and influenced by factors such as alignment, model size, and quantization.
  arXiv  Detail & Related papers  (2024-02-10T11:14:53Z)
• Code Representation Learning At Scale [75.04686476303436]
  We fuel code representation learning with a vast amount of code data via a two-stage pretraining scheme. We first train the encoders via a mix that leverages both randomness in masking language modeling and the structure aspect of programming language. We then enhance the representations via contrastive learning with hard negative and hard positive constructed in an unsupervised manner.
  arXiv  Detail & Related papers  (2024-02-02T22:19:15Z)
• Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency [71.42261918225773]
  Conceptually, LOCCO can be viewed as a form of self-learning where the semantic being trained is used to generate annotations for unlabeled text. As an added bonus, the annotations produced by LOCCO can be trivially repurposed to train a neural text generation model.
  arXiv  Detail & Related papers  (2023-05-31T16:47:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.