Papers by Suhas Kowshik

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Challenge:	Large language models (LLMs) have demonstrated remarkable performance in diverse tasks using zero-shot and few-shot prompting.
Approach:	They propose a method which generates data that is more diverse and faithful to the input prompt using a correlated sampling strategy.
Outcome:	The proposed method overcomes the complexity drawbacks of other guidance-based techniques and improves student metrics and intrinsic metrics upon competitive baselines across four datasets.