4 papers
TokenScope: Token-Level Explainability and Interpretability for Code-Oriented Tasks in Large Language Models
Amirreza Esmaeili, Fatemeh Fard
Understanding how Large Language Models (LLMs) make token-level decisions during code generation remains a major challenge for both researchers and practitioners. While recent tool…
Bias in the Loop: Auditing LLM-as-a-Judge for Software Engineering
Zixiao Zhao, Amirreza Esmaeili, Fatemeh Fard
Large Language Models are increasingly used as judges to evaluate code artifacts when exhaustive human review or executable test coverage is unavailable. LLM-judge is increasingly…
Analysis of AdvFusion: Adapter-based Multilingual Learning for Code Large Language Models
Amirreza Esmaeili, Fahd Seddik, Yongyi Ji +2
Programming languages can benefit from one another by utilizing a language model for software engineering tasks. Full fine-tuning and Parameter Efficient Fine-Tuning (PEFT) of Code…
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…