5 papers
Sparse Memory Finetuning as a Low-Forgetting Alternative to LoRA and Full Finetuning
Prakhar Gupta, Garv Shah, Satyam Goyal +1
Adapting a pretrained language model to a new task often hurts the general capabilities it already had, a problem known as catastrophic forgetting. Sparse Memory Finetuning (SMF) t…
DualDiffusion: A Speculative Decoding Strategy for Masked Diffusion Models
Satyam Goyal, Kushal Patel, Tanush Mittal +1
Masked Diffusion Models (MDMs) offer a promising alternative to autoregressive language models by enabling parallel token generation and bidirectional context modeling. However, th…
Improving Sparse Memory Finetuning
Satyam Goyal, Anirudh Kanchi, Garv Shah +1
Large Language Models (LLMs) are typically static after training, yet real-world applications require continual adaptation to new knowledge without degrading existing capabilities.…
FAIRFORMER: A transformer architecture for discrete fair division
Chris Mascioli, Satyam Goyal, Mithun Chakraborty
We propose a deep neural network-based solution to the problem of allocating indivisible goods under additive subjective valuations without monetary transfers, trading off economic…
IOLBENCH: Benchmarking LLMs on Linguistic Reasoning
Satyam Goyal, Soham Dan
Despite the remarkable advancements and widespread applications of deep neural networks, their ability to perform reasoning tasks remains limited, particularly in domains requiring…