1 citations · 1 across the 4 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…
AEGIS: From Clues to Verdicts -- Graph-Guided Deep Vulnerability Reasoning via Dialectics and Meta-Auditing
Sen Fang, Weiyuan Ding, Zhezhen Cao +2
Large Language Models (LLMs) are increasingly adopted for vulnerability detection, yet their reasoning remains fundamentally unsound. We identify a root cause shared by both major…
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…