fine-tuning

This paper presents a novel approach to software performance analysis by integrating traditional profiling techniques with a fine-tuned large language model (LLM), based on the Llama-3 model.  Addressing the challenges of manual profiling – such as overwhelming data volumes and the high expertise required to interpret performance metrics – the study introduces a lightweight AI-powered profiler trained on structured JSON-based profiling logs and code samples.  The model is fine-tuned using parameter-efficient methods (LoRA and QLoRA) to classify performance bottlenecks (

Processing Arabic dialects in Natural Language Processing (NLP) presents significant challenges due to linguistic diversity and the lack of standardized resources.  While Modern Standard Arabic (MSA) benefits from advanced NLP tools and extensive annotated datasets, dialects such as Moroccan Darija remain underrepresented.  This study introduces a BERT-based transcoding framework that bridges the gap between dialectal Arabic and MSA, enabling the use of pre-trained models optimized for MSA, such as AraBERT.  By integrating contextual multilingual embeddings, the propose

The increasing prevalence of automated social media accounts, or Social Media Bots (SMBs), presents significant challenges in maintaining authentic online discourse and preventing disinformation campaigns on social platforms.  This research introduces a novel multiclass classification framework for detecting and categorizing SMBs, leveraging fine-tuned transformer-based models.  In this study, we conducted a comprehensive comparative analysis of various transformer variants, including BERT, DistilBERT, RoBERTa, DeBERTa, XLNet, and ALBERT, to evaluate their efficacy in r