Cover Image

On the Effect of Token Merging on Pre-trained Models for Code

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Saad, Mootez, Li, Hao, Sharma, Tushar, Hassan, Ahmed E.
Format:	Preprint
Published:	2025
Subjects:	Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

CONCORD: Towards a DSL for Configurable Graph Code Representation
by: Saad, Mootez, et al.
Published: (2024)

Tu(r)ning AI Green: Exploring Energy Efficiency Cascading with Orthogonal Optimizations
by: Rajput, Saurabhsingh, et al.
Published: (2025)

Hierarchical Evaluation of Software Design Capabilities of Large Language Models of Code
by: Saad, Mootez, et al.
Published: (2025)

On Inter-dataset Code Duplication and Data Leakage in Large Language Models
by: López, José Antonio Hernández, et al.
Published: (2024)

ALPINE: An adaptive language-agnostic pruning method for language models for code
by: Saad, Mootez, et al.
Published: (2024)

SENAI: Towards Software Engineering Native Generative Artificial Intelligence
by: Saad, Mootez, et al.
Published: (2025)

Not All Tokens Matter: Data-Centric Optimization for Efficient Code Summarization
by: Afrin, Saima, et al.
Published: (2026)

CodeGreen: Towards Improving Precision and Portability in Software Energy Measurement
by: Rajput, Saurabhsingh, et al.
Published: (2026)

Towards Semantic Versioning of Open Pre-trained Language Model Releases on Hugging Face
by: Ajibode, Adekunle, et al.
Published: (2024)

Code Membership Inference for Detecting Unauthorized Data Use in Code Pre-trained Language Models
by: Zhang, Sheng, et al.
Published: (2023)

CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models
by: Yu, Hao, et al.
Published: (2023)

Energy Flow Graph: Modeling Software Energy Consumption
by: Rajput, Saurabhsingh, et al.
Published: (2026)

Natural Is The Best: Model-Agnostic Code Simplification for Pre-trained Large Language Models
by: Wang, Yan, et al.
Published: (2024)

Do AI Coding Agents Log Like Humans? An Empirical Study
by: Ouatiti, Youssef Esseddiq, et al.
Published: (2026)

AIDev: Studying AI Coding Agents on GitHub
by: Li, Hao, et al.
Published: (2026)

Coding-PTMs: How to Find Optimal Code Pre-trained Models for Code Embedding in Vulnerability Detection?
by: Zhao, Yu, et al.
Published: (2024)

Broken Windows: Exploring the Applicability of a Controversial Theory on Code Quality
by: Spinellis, Diomidis, et al.
Published: (2024)

Learning in the Wild: Towards Leveraging Unlabeled Data for Effectively Tuning Pre-trained Code Models
by: Gao, Shuzheng, et al.
Published: (2024)

Directional Diffusion-Style Code Editing Pre-training
by: Liang, Qingyuan, et al.
Published: (2025)

Bridge and Hint: Extending Pre-trained Language Models for Long-Range Code
by: Chen, Yujia, et al.
Published: (2024)

SynConfRoute: Syntax-Aware Routing for Efficient Code Completion with Small CodeLLMs
by: Thangarajah, Kishanthan, et al.
Published: (2026)

Can We Recycle Our Old Models? An Empirical Evaluation of Model Selection Mechanisms for AIOps Solutions
by: Lyu, Yingzhe, et al.
Published: (2025)

Context-Aware CodeLLM Eviction for AI-assisted Coding
by: Thangarajah, Kishanthan, et al.
Published: (2025)

On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub
by: Watanabe, Miku, et al.
Published: (2025)

Generating refactored code accurately using reinforcement learning
by: Palit, Indranil, et al.
Published: (2024)

Challenges of Using Pre-trained Models: the Practitioners' Perspective
by: Tan, Xin, et al.
Published: (2024)

Genetic Auto-prompt Learning for Pre-trained Code Intelligence Language Models
by: Feng, Chengzhe, et al.
Published: (2024)

Models Are Codes: Towards Measuring Malicious Code Poisoning Attacks on Pre-trained Model Hubs
by: Zhao, Jian, et al.
Published: (2024)

CLARA: A Developer's Companion for Code Comprehension and Analysis
by: Adnan, Ahmed, et al.
Published: (2025)

Structure-aware Fine-tuning for Code Pre-trained Models
by: Wu, Jiayi, et al.
Published: (2024)

Software Engineering and Foundation Models: Insights from Industry Blogs Using a Jury of Foundation Models
by: Li, Hao, et al.
Published: (2024)

LEANCODE: Understanding Models Better for Code Simplification of Pre-trained Large Language Models
by: Wang, Yan, et al.
Published: (2025)

A Validated Taxonomy on Software Energy Smells
by: Mehditabar, Mohammadjavad, et al.
Published: (2026)

The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering
by: Li, Hao, et al.
Published: (2025)

AgenticSZZ: Temporal Knowledge Graph-Guided Agentic Bug-Inducing Commit Identification
by: Shi, Yu, et al.
Published: (2026)

Adaptive Request Scheduling for CodeLLM Serving with SLA Guarantees
by: Chang, Shi, et al.
Published: (2025)

Improving the Ability of Pre-trained Language Model by Imparting Large Language Model's Experience
by: Yin, Xin, et al.
Published: (2024)

APPT: Boosting Automated Patch Correctness Prediction via Fine-tuning Pre-trained Models
by: Zhang, Quanjun, et al.
Published: (2023)

Token Sugar: Making Source Code Sweeter for LLMs through Token-Efficient Shorthand
by: Sun, Zhensu, et al.
Published: (2025)

A Universal Textual Merge Strategy Based on Tokens for Version Control Systems
by: Gu, Qiqi Jason, et al.
Published: (2026)