6 citations · 6 across the 1 of their papers we have counts for
4 papers
Matching Tasks to Objectives: Fine-Tuning and Prompt-Tuning Strategies for Encoder-Decoder Pre-trained Language Models
Ahmad Pouramini, Hesham Faili
Prompt-based learning has emerged as a dominant paradigm in natural language processing. This study explores the impact of diverse pre-training objectives on the performance of enc…
CrossPT: Exploring Cross-Task Transferability through Multi-Task Prompt Tuning
Ahmad Pouramini, Hesham Faili
Prompt tuning offers a parameter-efficient way to adapt large pre-trained language models to new tasks, but most existing approaches are designed for single-task settings, failing…
Enhancing Few-Shot Transfer Learning with Optimized Multi-Task Prompt Tuning through Modular Prompt Composition
Ahmad Pouramini, Hesham Faili
In recent years, multi-task prompt tuning has garnered considerable attention for its inherent modularity and potential to enhance parameter-efficient transfer learning across dive…
PerMedCQA: Benchmarking Large Language Models on Medical Consumer Question Answering in Persian Language
Naghmeh Jamali, Milad Mohammadi, Danial Baledi +2
Medical consumer question answering (CQA) is crucial for empowering patients by providing personalized and reliable health information. Despite recent advances in large language mo…