Publications (7)
DocAgent: A Multi-Agent System for Automated Code Documentation Generation
Dayu Yang, Antoine Simoulin, Xin Qian +4
High-quality code documentation is crucial for software development especially in the era of AI. However, generating it automatically using Large Language Models (LLMs) remains cha…
Memory-Efficient Fine-Tuning of Transformers via Token Selection
Antoine Simoulin, Namyong Park, Xiaoyi Liu +1
Fine-tuning provides an effective means to specialize pre-trained models for various downstream tasks. However, fine-tuning often incurs high memory overhead, especially for large…
GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
Namyong Park, Ryan Rossi, Xing Wang +3
The choice of a graph learning (GL) model (i.e., a GL algorithm and its hyperparameter settings) has a significant impact on the performance of downstream tasks. However, selecting…
An innovative solution for breast cancer textual big data analysis
Nicolas Thiebaut, Antoine Simoulin, Karl Neuberger +5
The digitalization of stored information in hospitals now allows for the exploitation of medical data in text format, as electronic health records (EHRs), initially gathered for ot…
Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs
Dayu Yang, Tianyang Liu, Daoan Zhang +8
In large language models (LLMs), code and reasoning reinforce each other: code offers an abstract, modular, and logic-driven structure that supports reasoning, while reasoning tran…
Enhancing Open-Vocabulary Object Detection through Multi-Level Fine-Grained Visual-Language Alignment
Tianyi Zhang, Antoine Simoulin, Kai Li +5
Traditional object detection systems are typically constrained to predefined categories, limiting their applicability in dynamic environments. In contrast, open-vocabulary object d…