2 citations · 6 across the 15 of their papers we have counts for
11 papers · 1 filter
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…
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…