mlsanityData to 14 Oct 2026

C-ing Clearly: Enhanced Binary Code Explanations using C code

arXiv 2512.14500 ↗First posted 16 Dec 2025cs.CL cs.LGcited by 0

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Abstract (arXiv)

Large Language Models (LLMs) typically excel at coding tasks involving high-level programming languages, as opposed to lower-level programming languages, such as assembly. We propose a synthetic data generation method named C-ing Clearly, which leverages the corresponding C code to enhance an LLM's understanding of assembly. By fine-tuning on data generated through our method, we demonstrate improved LLM performance for binary code summarization and vulnerability detection. Our approach demonstrates consistent gains across different LLM families and model sizes.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 32 references that are in the map. First posted on arXiv 16 Dec 2025.

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References · 24

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  5. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  6. 2019The Curious Case of Neural Text Degenerationcited by 2,916
  7. 2024Vulnerability Detection with Code Language Models: How Far Are We?cited by 139
  8. 2024HelpSteer2-Preference: Complementing Ratings with Preferencescited by 130
  9. 2018Automated Vulnerability Detection in Source Code Using Deep Representation Learningcited by 114
  10. 2023DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detectioncited by 102
  11. 2020Retrieval-Augmented Generation for Code Summarization via Hybrid GNNcited by 89
  12. 2024LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoningcited by 59
12 more
  1. 2023Prompt-Enhanced Software Vulnerability Detection Using ChatGPTcited by 51
  2. 2022jTrans: Jump-Aware Transformer for Binary Code Similaritycited by 44
  3. 2022Transformer-Based Language Models for Software Vulnerability Detectioncited by 39
  4. 2024Meta Large Language Model Compiler: Foundation Models of Compiler Optimizationcited by 39
  5. 2023Measuring The Impact Of Programming Language Distributioncited by 32
  6. 2023Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binariescited by 26
  7. 2024CLAP: Learning Transferable Binary Code Representations with Natural Language Supervisioncited by 17
  8. 2024DLAP: A Deep Learning Augmented Large Language Model Prompting Framework for Software Vulnerability Detectioncited by 13
  9. 2023CP-BCS: Binary Code Summarization Guided by Control Flow Graph and Pseudo Codecited by 11
  10. 2024MALSIGHT: Exploring Malicious Source Code and Benign Pseudocode for Iterative Binary Malware Summarizationcited by 5
  11. 2025ASMA-Tune: Unlocking LLMs' Assembly Code Comprehension via Structural-Semantic Instruction Tuningcited by 4
  12. 2024Bridge-Coder: Unlocking LLMs' Potential to Overcome Language Gaps in Low-Resource Codecited by 3

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