most citedParameter-Efficient Fine-Tuning of Large Language Models using Semantic Knowledge Tuning

2 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2025

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…

cs.CL2025

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…

cs.CL2024★ 2 cited

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…

cs.CL2024

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…

cs.CL2024★ 1 cited

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…

cs.CL2024★ 1 cited

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…