6 papers · 1 filter
Context-Augmented Code Generation Using Programming Knowledge Graphs
Shahd Seddik, Fahd Seddik, Iman Saberi +3
Large Language Models (LLMs) excel at code generation but struggle with complex problems. Retrieval-Augmented Generation (RAG) mitigates this issue by integrating external knowledg…
Context-Augmented Code Generation Using Programming Knowledge Graphs
Iman Saberi, Fatemeh Fard
Large Language Models (LLMs) and Code-LLMs (CLLMs) have significantly improved code generation, but, they frequently face difficulties when dealing with challenging and complex pro…
Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to R
Amirreza Esmaeili, Iman Saberi, Fatemeh H. Fard
Parameter Efficient Fine-Tuning (PEFT) methods are proposed as an alternative fine-tuning approach for Large Language Models (LLM) to minimize high training costs. While prior rese…
Utilization of Pre-trained Language Model for Adapter-based Knowledge Transfer in Software Engineering
Iman Saberi, Fatemeh Fard, Fuxiang Chen
Software Engineering (SE) Pre-trained Language Models (PLMs), such as CodeBERT, are pre-trained on large code corpora, and their learned knowledge has shown success in transferring…
AdvFusion: Adapter-based Knowledge Transfer for Code Summarization on Code Language Models
Iman Saberi, Amirreza Esmaeili, Fatemeh Fard +1
Programming languages can benefit from one another by utilizing a pre-trained model for software engineering tasks such as code summarization and method name prediction. While full…
Model-Agnostic Syntactical Information for Pre-Trained Programming Language Models
Iman Saberi, Fatemeh H. Fard
Pre-trained Programming Language Models (PPLMs) achieved many recent states of the art results for many code-related software engineering tasks. Though some studies use data flow o…