Papers by Lilly Kumari
An End-to-End Submodular Framework for Data-Efficient In-Context Learning (2024.findings-naacl)
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| Challenge: | Recent advances in natural language tasks leverage the emergent In-Context Learning ability of pretrained Large Language Models (LLMs). |
| Approach: | They propose a framework for exemplar selection for in-context learning that uses a pool-based active learning approach to select Diverse and informative exemplars from the target tasks’ unlabeled pool. |
| Outcome: | The proposed framework outperforms existing methods for data annotation and similarity-based methods for test query-specific exemplar retrieval on 7 different NLP datasets and 5 LLMs of varying complexities. |