10 papers · 1 filter
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…
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…
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…
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…
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,…