220 citations · 1k across the 53 of their papers we have counts for
56 papers
When Prompt Under-Specification Improves Code Correctness: An Exploratory Study of Prompt Wording and Structure Effects on LLM-Based Code Generation
Amal AKLI, Mike PAPADAKIS, Maxime CORDY +1
Large language models are increasingly used for code generation, yet the correctness of their outputs depends not only on model capability but also on how tasks are specified. Prio…
One Model, Many Skills: Parameter-Efficient Fine-Tuning for Multitask Code Analysis
Amal Akli, Maxime Cordy, Mike Papadakis +1
Large language models have recently surpassed specialized systems on code generation, yet their effectiveness on other code-analysis tasks remains less clear. At the same time, mul…
FedSparQ: Adaptive Sparse Quantization with Error Feedback for Robust & Efficient Federated Learning
Chaimaa Medjadji, Sadi Alawadi, Feras M. Awaysheh +3
Federated Learning (FL) enables collaborative model training across decentralized clients while preserving data privacy by keeping raw data local. However, FL suffers from signific…
Leveraging External Factors in Household-Level Electrical Consumption Forecasting using Hypernetworks
Fabien Bernier, Maxime Cordy, Yves Le Traon
Accurate electrical consumption forecasting is crucial for efficient energy management and resource allocation. While traditional time series forecasting relies on historical patte…
HInter: Exposing Hidden Intersectional Bias in Large Language Models
Badr Souani, Ezekiel Soremekun, Mike Papadakis +3
Large Language Models (LLMs) may portray discrimination towards certain individuals, especially those characterized by multiple attributes (aka intersectional bias). Discovering in…
Active Code Learning: Benchmarking Sample-Efficient Training of Code Models
Qiang Hu, Yuejun Guo, Xiaofei Xie +4
The costly human effort required to prepare the training data of machine learning (ML) models hinders their practical development and usage in software engineering (ML4Code), espec…