Papers by Sunny Rajagopalan

2 papers
DynaMaR: Dynamic Prompt with Mask Token Representation (2022.emnlp-industry)

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Challenge: Recent research shows that large language models pretrained using unsupervised approaches can achieve significant performance improvement on many downstream tasks.
Approach: They propose an unsupervised approach to fine-tuning large language models using unsupervised approaches to many downstream tasks.
Outcome: The proposed approach improves on four e-commerce applications and can achieve an average improvement of 10% in few-shot settings and 3.7% in data-rich settings over the standard approach.
Asynchronous Convergence in Multi-Task Learning via Knowledge Distillation from Converged Tasks (2022.naacl-industry)

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Challenge: Multi-task learning (MTL) aims to solve multiple tasks by sharing a base representation among them.
Approach: They propose an approach that allows for "asynchronous" convergence among the tasks where each task can converge on its own schedule.
Outcome: The proposed method outperforms existing methods in two 5-task MTL setups.

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