2 citations · 6 across the 15 of their papers we have counts for
6 papers · 2 filters
Does Self-Attention Need Separate Weights in Transformers?
Md Kowsher, Nusrat Jahan Prottasha, Chun-Nam Yu +2
The success of self-attention lies in its ability to capture long-range dependencies and enhance context understanding, but it is limited by its computational complexity and challe…
Parameter-Efficient Fine-Tuning of Large Language Models using Semantic Knowledge Tuning
Nusrat Jahan Prottasha, Asif Mahmud, Md. Shohanur Islam Sobuj +4
Large Language Models (LLMs) are gaining significant popularity in recent years for specialized tasks using prompts due to their low computational cost. Standard methods like prefi…
RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates
Md Kowsher, Tara Esmaeilbeig, Chun-Nam Yu +3
We propose RoCoFT, a parameter-efficient fine-tuning method for large-scale language models (LMs) based on updating only a few rows and columns of the weight matrices in transforme…
Propulsion: Steering LLM with Tiny Fine-Tuning
Md Kowsher, Nusrat Jahan Prottasha, Prakash Bhat
The rapid advancements in Large Language Models (LLMs) have revolutionized natural language processing (NLP) and related fields. However, fine-tuning these models for specific task…
Token Trails: Navigating Contextual Depths in Conversational AI with ChatLLM
Md. Kowsher, Ritesh Panditi, Nusrat Jahan Prottasha +3
Conversational modeling using Large Language Models (LLMs) requires a nuanced understanding of context to generate coherent and contextually relevant responses. In this paper, we p…
L-TUNING: Synchronized Label Tuning for Prompt and Prefix in LLMs
Md. Kowsher, Md. Shohanur Islam Sobuj, Asif Mahmud +2
Efficiently fine-tuning Large Language Models (LLMs) for specific tasks presents a considerable challenge in natural language processing. Traditional methods, like prompt or prefix…