Cover Image

An Initial Exploration of Contrastive Prompt Tuning to Generate Energy-Efficient Code

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Weidmann, Sophie, Castor, Fernando
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code
by: Solovyeva, Lola, et al.
Published: (2025)

DDPT: Diffusion-Driven Prompt Tuning for Large Language Model Code Generation
by: Li, Jinyang, et al.
Published: (2025)

One Model, Many Skills: Parameter-Efficient Fine-Tuning for Multitask Code Analysis
by: Akli, Amal, et al.
Published: (2026)

Towards Green AI: Decoding the Energy of LLM Inference in Software Development
by: Solovyeva, Lola, et al.
Published: (2026)

Selective Prompt Anchoring for Code Generation
by: Tian, Yuan, et al.
Published: (2024)

Instruction Tuning for Secure Code Generation
by: He, Jingxuan, et al.
Published: (2024)

Benchmarking Reward Hack Detection in Code Environments via Contrastive Analysis
by: Deshpande, Darshan, et al.
Published: (2026)

How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark
by: Qiu, Ruizhong, et al.
Published: (2024)

Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures
by: Alizadeh, Negar, et al.
Published: (2024)

Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Study
by: Storhaug, André, et al.
Published: (2024)

Free and Customizable Code Documentation with LLMs: A Fine-Tuning Approach
by: Chakrabarty, Sayak, et al.
Published: (2024)

Prompting Techniques for Secure Code Generation: A Systematic Investigation
by: Tony, Catherine, et al.
Published: (2024)

Keeping Code-Aware LLMs Fresh: Full Refresh, In-Context Deltas, and Incremental Fine-Tuning
by: Sharma, Pradeep Kumar, et al.
Published: (2025)

CodeTaste: Can LLMs Generate Human-Level Code Refactorings?
by: Thillen, Alex, et al.
Published: (2026)

Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models
by: Chen, Jie, et al.
Published: (2024)

Operational Robustness of LLMs on Code Generation
by: Paul, Debalina Ghosh, et al.
Published: (2026)

Can Coding Agents Be General Agents?
by: Ivanov, Maksim, et al.
Published: (2026)

Optimizing AI-Assisted Code Generation
by: Torka, Simon, et al.
Published: (2024)

Code Generation by Differential Test Time Scaling
by: He, Yifeng, et al.
Published: (2026)

Clover: Closed-Loop Verifiable Code Generation
by: Sun, Chuyue, et al.
Published: (2023)

Functional Overlap Reranking for Neural Code Generation
by: To, Hung Quoc, et al.
Published: (2023)

A Theoretical Analysis of Test-Driven Code Generation
by: Menet, Nicolas, et al.
Published: (2026)

Protocode: Prototype-Driven Interpretability for Code Generation in LLMs
by: Bodla, Krishna Vamshi, et al.
Published: (2025)

More with Less: An Empirical Study of Turn-Control Strategies for Efficient Coding Agents
by: Gao, Pengfei, et al.
Published: (2025)

CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X
by: Zheng, Qinkai, et al.
Published: (2023)

An Initial Exploration of Fine-tuning Small Language Models for Smart Contract Reentrancy Vulnerability Detection
by: Pofcher, Ignacio Mariano Andreozzi, et al.
Published: (2025)

Enhancing LLM-Based Test Generation by Eliminating Covered Code
by: Xu, WeiZhe, et al.
Published: (2026)

Exploring Pass-Rate Reward in Reinforcement Learning for Code Generation
by: Li, Xin-Ye, et al.
Published: (2026)

Semantic Voting: Execution-Grounded Consensus for LLM Code Generation
by: Jiang, Shan, et al.
Published: (2026)

Deep-Bench: Deep Learning Benchmark Dataset for Code Generation
by: Daghighfarsoodeh, Alireza, et al.
Published: (2025)

Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code
by: Galimzyanov, Timur, et al.
Published: (2024)

Supersonic: Learning to Generate Source Code Optimizations in C/C++
by: Chen, Zimin, et al.
Published: (2023)

Co-Located Tests, Better AI Code: How Test Syntax Structure Affects Foundation Model Code Generation
by: Jacopin, Éric
Published: (2026)

ReqBrain: Task-Specific Instruction Tuning of LLMs for AI-Assisted Requirements Generation
by: Habib, Mohammad Kasra, et al.
Published: (2025)

VeriScale: Adversarial Test-Suite Scaling for Verifiable Code Generation
by: Bai, Yifan, et al.
Published: (2026)

VeriContest: A Competitive-Programming Benchmark for Verifiable Code Generation
by: Xie, Zichen, et al.
Published: (2026)

LiCoEval: Evaluating LLMs on License Compliance in Code Generation
by: Xu, Weiwei, et al.
Published: (2024)

Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization
by: Chen, Zhi, et al.
Published: (2024)

DevBench: A Realistic, Developer-Informed Benchmark for Code Generation Models
by: Kumarappan, Adarsh, et al.
Published: (2026)

The Counterfeit Conundrum: Can Code Language Models Grasp the Nuances of Their Incorrect Generations?
by: Gu, Alex, et al.
Published: (2024)