1 citations · 2 across the 7 of their papers we have counts for
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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
Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias. While this behavi…
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…
Smaller = Weaker? Benchmarking Robustness of Quantized LLMs in Code Generation
Sen Fang, Weiyuan Ding, Antonio Mastropaolo +1
Quantization has emerged as a mainstream method for compressing Large Language Models (LLMs), reducing memory requirements and accelerating inference without architectural modifica…
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…