FORGE 2024
Sun 14 Apr 2024 Lisbon, Portugal
co-located with ICSE 2024

Large Language Models (LLMs) are revolutionizing the landscape of Artificial Intelligence (AI) due to recent technological breakthroughs. Their remarkable success in aiding various Software Engineering (SE) tasks through AI-powered tools and assistants has led to the integration of LLMs as active contributors within development teams, ushering in novel modes of communication and collaboration. However, great power comes with great responsibility: ensuring that these models meet fundamental ethical principles such as fairness is still an open challenge. In this light, our vision paper analyzes the existing body of knowledge to propose a conceptual model designed to frame ethical, social, and cultural considerations that researchers and practitioners should consider when defining, employing, and validating LLM-based approaches for software engineering tasks.

Sun 14 Apr

Displayed time zone: Lisbon change

14:00 - 15:30
Keynote 2 & Properties of Foundation ModelsResearch Track / Keynotes at Luis de Freitas Branco
Chair(s): David Lo Singapore Management University, Feifei Niu University of Ottawa
14:00
40m
Keynote
Keynote 2: Towards an Interpretable Science of Deep Learning for Software Engineering: A Causal Inference View
Keynotes
Denys Poshyvanyk William & Mary
14:40
14m
Full-paper
Exploring the Impact of the Output Format on the Evaluation of Large Language Models for Code TranslationFull Paper
Research Track
Marcos Macedo Queen's University, Kingston, Ontario, Yuan Tian Queen's University, Kingston, Ontario, Filipe Cogo Centre for Software Excellence, Huawei Canada, Bram Adams Queen's University
Pre-print
14:54
7m
Short-paper
Is Attention All You Need? Toward a Conceptual Model for Social Awareness in Large Language ModelsNew Idea Paper
Research Track
Gianmario Voria University of Salerno, Gemma Catolino University of Salerno, Fabio Palomba University of Salerno
Pre-print
15:01
14m
Full-paper
An Exploratory Investigation into Code License Infringements in Large Language Model Training DatasetsFull Paper
Research Track
Jonathan Katzy Delft University of Technology, Răzvan Mihai Popescu Delft University of Technology, Arie van Deursen Delft University of Technology, Maliheh Izadi Delft University of Technology
15:15
15m
Other
Discussion
Research Track