2 citations · 6 across the 15 of their papers we have counts for
15 papers
Monkey Jump : MoE-Style PEFT for Efficient Multi-Task Learning
Nusrat Jahan Prottasha, Md Kowsher, Chun-Nam Yu +2
Mixture-of-experts variants of parameter-efficient fine-tuning enable per-token specialization, but they introduce additional trainable routers and expert parameters, increasing me…
SliceFine: The Universal Winning-Slice Hypothesis for Pretrained Networks
Md Kowsher, Ali O. Polat, Ehsan Mohammady Ardehaly +4
This paper presents a theoretical framework explaining why fine tuning small, randomly selected subnetworks (slices) within pre trained models can be sufficient for downstream adap…
Explainable Detection of Implicit Influential Patterns in Conversations via Data Augmentation
Sina Abdidizaji, Md Kowsher, Niloofar Yousefi +1
In the era of digitalization, as individuals increasingly rely on digital platforms for communication and news consumption, various actors employ linguistic strategies to influence…
FlowNIB: An Information Bottleneck Analysis of Bidirectional vs. Unidirectional Language Models
Md Kowsher, Nusrat Jahan Prottasha, Shiyun Xu +4
Bidirectional language models have better context understanding and perform better than unidirectional models on natural language understanding tasks, yet the theoretical reasons b…
PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models
Nusrat Jahan Prottasha, Upama Roy Chowdhury, Shetu Mohanto +7
Large models such as Large Language Models (LLMs) and Vision Language Models (VLMs) have transformed artificial intelligence, powering applications in natural language processing,…
User Profile with Large Language Models: Construction, Updating, and Benchmarking
Nusrat Jahan Prottasha, Md Kowsher, Hafijur Raman +4
User profile modeling plays a key role in personalized systems, as it requires building accurate profiles and updating them with new information. In this paper, we present two high…
Predicting Through Generation: Why Generation Is Better for Prediction
Md Kowsher, Nusrat Jahan Prottasha, Prakash Bhat +6
This paper argues that generating output tokens is more effective than using pooled representations for prediction tasks because token-level generation retains more mutual informat…
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…
LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting
Md Kowsher, Md. Shohanur Islam Sobuj, Nusrat Jahan Prottasha +3
Time series forecasting remains a challenging task, particularly in the context of complex multiscale temporal patterns. This study presents LLM-Mixer, a framework that improves fo…
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…
Impact Learning: A Learning Method from Features Impact and Competition
Nusrat Jahan Prottasha, Saydul Akbar Murad, Abu Jafar Md Muzahid +5
Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called…