From the 1 of 7 linked papers with an AI index.
7 papers
Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality
Saima Afrin, Alessandro Midolo, Camilo Escobar-Velásquez +5
The paper introduces a curated multilingual benchmark to study how the natural language of prompts influences code generation quality of large language models, evaluating functiona…
Long Live The Balance: Information Bottleneck Driven Tree-based Policy Optimization
Hao Jiang, Shurui Li, Tianpeng Bu +7
Recent advances in online reinforcement learning (RL) for large language models (LLMs) have demonstrated promising performance in complex reasoning tasks. However, they often exhib…
EVALOOOP: A Self-Consistency-Centered Framework for Assessing Large Language Model Robustness in Programming
Sen Fang, Weiyuan Ding, Mengshi Zhang +2
Evaluating the programming robustness of large language models (LLMs) is paramount for ensuring their reliability in AI-based software development. However, adversarial attacks exh…
Understanding Privacy Risks in Code Models Through Training Dynamics: A Causal Approach
Hua Yang, Alejandro Velasco, Sen Fang +2
Large language models for code (LLM4Code) have greatly improved developer productivity but also raise privacy concerns due to their reliance on open-source repositories containing…
How Quantization Impacts Privacy Risk on LLMs for Code?
Md Nazmul Haque, Hua Yang, Zhou Yang +1
Large language models for code (LLMs4Code) rely heavily on massive training data, including sensitive data, such as cloud service credentials of the projects and personal identifia…
Is Quantization a Deal-breaker? Empirical Insights from Large Code Models
Saima Afrin, Bowen Xu, Antonio Mastropaolo
The growing scale of large language models (LLMs) not only demands extensive computational resources but also raises environmental concerns due to their increasing carbon footprint…