Papers by Vivek Subramanian
Methods for Numeracy-Preserving Word Embeddings (2020.emnlp-main)
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Dhanasekar Sundararaman, Shijing Si, Vivek Subramanian, Guoyin Wang, Devamanyu Hazarika, Lawrence Carin
| Challenge: | Word embedding models capture semantic relationships between words but fail to capture numerical properties associated with numbers. |
| Approach: | They propose a method to assign and learn embeddings for numbers using word embedders. |
| Outcome: | The proposed model outperforms pre-trained word embedding models across multiple examples of two tasks. |
LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints (2024.findings-emnlp)
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Thomas Palmeira Ferraz, Kartik Mehta, Yu-Hsiang Lin, Haw-Shiuan Chang, Shereen Oraby, Sijia Liu, Vivek Subramanian, Tagyoung Chung, Mohit Bansal, Nanyun Peng
| Challenge: | Recent studies have shown that LLMs struggle with instructions containing multiple constraints. |
| Approach: | They propose a self-correction pipeline that decomposes the original instruction into a list of constraints and uses a Critic model to decide when and where the LLM’s response needs refinement. |
| Outcome: | The proposed model outperforms GPT-4 on RealInstruct and IFEval even with weak feedback. |
SpanPredict: Extraction of Predictive Document Spans with Neural Attention (2021.naacl-main)
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| Challenge: | identifying predictive text in clinical notes can be as important as the predictions themselves . identifying specific content in clinical note descriptions may illuminate previously unknown risk factors . |
| Approach: | They propose a method for identifying predictive text in clinical notes . they use linear attention to formalize the problem as predictive extraction . |
| Outcome: | The proposed model preserves differentiability and allows scalable inference via stochastic gradient descent. |