78 citations · 79 across the 7 of their papers we have counts for
7 papers
Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations
Nada Zine, Tristan Coignion, Vincenzo Stoico +4
Large Language Models are reshaping how software is developed and maintained. They are typically deployed in production using inference engines such as vLLM, which can efficiently…
Imperfect Visual Verification for Code Edition : A Case Study on TikZ
Charly Reux, Mathieu Acher, Djamel Eddine Khelladi +2
LLMs have significantly advanced code generation, enabling the synthesis of functional programs. While recent systems achieve strong performance on many coding benchmarks, tasks in…
Pimp My LLM: Leveraging Variability Modeling to Tune Inference Hyperparameters
Nada Zine, Clément Quinton, Romain Rouvoy
Large Language Models (LLMs) are being increasingly used across a wide range of tasks. However, their substantial computational demands raise concerns about the energy efficiency a…
LLM Code Customization with Visual Results: A Benchmark on TikZ
Charly Reux, Mathieu Acher, Djamel Eddine Khelladi +2
With the rise of AI-based code generation, customizing existing code out of natural language instructions to modify visual results -such as figures or images -has become possible,…
Green My LLM: Studying the key factors affecting the energy consumption of code assistants
Tristan Coignion, Clément Quinton, Romain Rouvoy
In recent years,Large Language Models (LLMs) have significantly improved in generating high-quality code, enabling their integration into developers' Integrated Development Environ…
Exploring Performance Trade-offs in JHipster
Edouard Guégain, Alexandre Bonvoisin, Clément Quinton +2
The performance of software systems remains a persistent concern in the field of software engineering. While traditional metrics like binary size and execution time have long been…
A Performance Study of LLM-Generated Code on Leetcode
Tristan Coignion, Clément Quinton, Romain Rouvoy
This study evaluates the efficiency of code generation by Large Language Models (LLMs) and measures their performance against human-crafted solutions using a dataset from Leetcode.…