arXiv AI

Enhancing the Non-Functional Quality Compliance of LLM-Generated Code through Quality-Aware Preference Learning

arXiv:2503. 09020v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have been widely adopted in commercial code completion engines, significantly enhancing coding efficiency and productivity.

arXiv AI

Jun 30

Reward-Free Code Alignment from Pretrained or Fine-Tuned LLM: Unpacking the Trade-offs for Code Generation

arXiv:2606. 28998v1 Announce Type: cross Abstract: Large Language Model (LLM) alignment trains an LLM using preference data to produce outputs that better meet established quality standards.

By Gias Uddin, Sanjeepan Sivapiran

llms reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Aug 11

Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring

arXiv:2605. 00754v4 Announce Type: replace-cross Abstract: Reward models (RMs) have become an indispensable fixture of the language model (LM) post-training playbook, enabling policy alignment and test-time scaling.

By Indraneil Paul, Goran Glava\v{s}, Iryna Gurevych

llms reinforcement-learning benchmarks safety

arXiv AI

Jun 16

FasterPy: An LLM-based Code Execution Efficiency Optimization Framework

arXiv:2512. 22827v2 Announce Type: replace-cross Abstract: Code often suffers from performance bugs.

By Yue Wu, Minghao Han, Ruiyin Li, Peng Liang, Amjed Tahir, Zengyang Li, Qiong Feng, Mojtaba Shahin

llms rag fine-tuning benchmarks

arXiv AI

Jun 8

SWE-IF: Aligning Code Evaluation with Human Preference

arXiv:2510. 07315v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have catalyzed vibe coding, where users leverage LLMs to generate and iteratively refine code through natural language interactions until it passes their vibe check.

By Ming Zhong, Xiang Zhou, Ting-Yun Chang, Qingze Wang, Nan Xu, Xiance Si, Dan Garrette, Shyam Upadhyay, Jeremiah Liu, Jiawei Han, Benoit Schillings, Jiao Sun

llms

arXiv AI

1d ago

Self-Bootstrapping Automated Program Repair: Using LLMs to Generate and Evaluate Synthetic Training Data for Bug Repair

arXiv:2505. 07372v3 Announce Type: replace-cross Abstract: This paper presents a novel methodology for enhancing Automated Program Repair (APR) through synthetic data generation utilizing Large Language Models (LLMs).

By David de-Fitero-Dominguez, Antonio Garcia-Cabot, Eva Garcia-Lopez

llms benchmarks

arXiv AI

1d ago

When Uncertainty Isn't Enough: An Empirical Study of Self-Correction in Code Generation

arXiv:2608. 14659v1 Announce Type: new Abstract: Large language models for code generation often produce incorrect solutions without reliable indicators of failure.

By Pranav Rakasi, Maanas Lalwani, Arnav Srivastava, Arya Palanivel, Tinuade Adeleke, Ruizhe Li, Sean Wu

llms benchmarks

arXiv AI

2d ago

The Fools are Certain; the Wise are Doubtful: Exploring LLM Confidence in Code Completion

arXiv:2508. 16131v3 Announce Type: replace-cross Abstract: Code completion entails the task of providing missing tokens given a surrounding context.

By Zoe Kotti, Konstantina Dritsa, Diomidis Spinellis, Panos Louridas

llms fine-tuning safety

arXiv AI

Jun 16

CHILLGuard: Towards Fine-Grained Chinese LLM Safety Guardrail with Scalable Data Construction and Model-aware Preference Alignment

arXiv:2606. 15396v1 Announce Type: cross Abstract: Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns.

By Wenbo Yu, Bohua Wang, Hao Fang, Kuofeng Gao, Jingru Zeng, Xiaochen Yang, Tianyi Zhang, Xiaoxiao Ma, Jiawei Kong, Hao Wu, Bin Chen, Shu-Tao Xia, Min Zhang

llms rag benchmarks safety

arXiv AI

Jun 8

CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval

arXiv:2506. 11066v3 Announce Type: replace-cross Abstract: Code retrieval is essential in modern software development, as it boosts code reuse and accelerates debugging.

By Jiahui Geng, Fengyu Cai, Shaobo Cui, Qing Li, Liangwei Chen, Chenyang Lyu, Haonan Li, Derui Zhu, Walter Pretschner, Heinz Koeppl, Fakhri Karray

benchmarks

arXiv Machine Learning

Aug 11

MetaLint: Easy-to-Hard Generalization for Code Linting

arXiv:2507. 11687v5 Announce Type: replace-cross Abstract: Large language models excel at code generation but struggle with code linting, particularly in generalizing to unseen or evolving best practices beyond those observed during training.

By Atharva Naik, Lawanya Baghel, Dhakshin Govindarajan, Darsh Agrawal, Yiqing Xie, Daniel Fried, Carolyn Rose

llms fine-tuning benchmarks

arXiv AI

Jun 6

Towards the Readability of LLM-Generated Codes through Multitask Representation Engineering

arXiv:2606. 06214v1 Announce Type: cross Abstract: Correctness and readability are key measures of code quality, respectively ensuring functional fidelity and ease of comprehension.

By Huifan Gao, Liuhua He, Yinghui Pan, Shenbao Yu, Yifeng Zeng, Shengchao Qin, Weidi Sun

llms

arXiv AI

Jul 17

Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality

arXiv:2607. 14816v1 Announce Type: cross Abstract: Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias.

By Saima Afrin, Alessandro Midolo, Camilo Escobar-Vel\'asquez, Mario Linares-V\'asquez, Weiyuan Ding, Bowen Xu, Massimiliano Di Penta, Antonio Mastropaolo

llms benchmarks safety

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