64 citations · 147 across the 44 of their papers we have counts for
5 papers · 1 filter
LAWCAT: Efficient Distillation from Quadratic to Linear Attention with Convolution across Tokens for Long Context Modeling
Zeyu Liu, Souvik Kundu, Lianghao Jiang +5
Although transformer architectures have achieved state-of-the-art performance across diverse domains, their quadratic computational complexity with respect to sequence length remai…
Top-H Decoding: Adapting the Creativity and Coherence with Bounded Entropy in Text Generation
Erfan Baghaei Potraghloo, Seyedarmin Azizi, Souvik Kundu +1
Large language models (LLMs), despite their impressive performance across a wide range of tasks, often struggle to balance two competing objectives in open-ended text generation: f…
LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation
Seyedarmin Azizi, Souvik Kundu, Massoud Pedram
Low-rank adaptation (LoRA) has become the default approach to fine-tune large language models (LLMs) due to its significant reduction in trainable parameters. However, trainable pa…
AFLoRA: Adaptive Freezing of Low Rank Adaptation in Parameter Efficient Fine-Tuning of Large Models
Zeyu Liu, Souvik Kundu, Anni Li +3
We present a novel Parameter-Efficient Fine-Tuning (PEFT) method, dubbed as Adaptive Freezing of Low Rank Adaptation (AFLoRA). Specifically, for each pre-trained frozen weight tens…
Sensi-BERT: Towards Sensitivity Driven Fine-Tuning for Parameter-Efficient BERT
Souvik Kundu, Sharath Nittur Sridhar, Maciej Szankin +1
Large pre-trained language models have recently gained significant traction due to their improved performance on various down-stream tasks like text classification and question ans…