Papers by Karen Hovsepian
Tagging-Augmented Generation: Assisting Language Models in Finding Intricate Knowledge In Long Contexts (2025.emnlp-industry)
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Anwesan Pal, Karen Hovsepian, Tinghao Guo, Mengnan Zhao, Somendra Tripathi, Nikos Kanakaris, George Mihaila, Sumit Nigam
| Challenge: | Recent studies into effective context lengths of flagship large language models (LLMs) have revealed major limitations in effective question answering (QA) and reasoning over long and complex contexts for even the largest and most impressive cadre of models. |
| Approach: | They propose a lightweight data augmentation strategy that boosts LLM performance in long-context scenarios without degrading and altering the integrity and composition of retrieved documents. |
| Outcome: | The proposed strategy boosts performance in long-context scenarios without degrading and altering the integrity and composition of retrieved documents. |
Semantic matching for text classification with complex class descriptions (2023.emnlp-main)
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| Challenge: | Existing methods for text classification support zero-shot learning but not both . Existing approaches do not support zero or few-shot, and are insufficient for complex classes . |
| Approach: | They propose a method which rapidly adapts from seen classes to new/unseen ones . they use labels and complex class descriptions to perform zero- and few-shot learning . |
| Outcome: | The proposed method beats baselines on complex class descriptions by 22.48% . it also improves zero-shot learning by 4.29% . |