Papers by Jennifer Dy
QueryForm: A Simple Zero-shot Form Entity Query Framework (2023.findings-acl)
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Zifeng Wang, Zizhao Zhang, Jacob Devlin, Chen-Yu Lee, Guolong Su, Hao Zhang, Jennifer Dy, Vincent Perot, Tomas Pfister
| Challenge: | Form-like document understanding is a key yet under-investigated problem . endlessly training specialized models on new document types is not scalable in many practical scenarios. |
| Approach: | They propose to use large-scale query-entity pairs generated from form-like webpages to pre-train QueryForm. |
| Outcome: | The proposed framework sets state-of-the-art average F1 score on XFUND and Payment benchmarks. |
Linear-Time Demonstration Selection for In-Context Learning via Gradient Estimation (2025.emnlp-main)
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| Challenge: | Existing methods to select demonstration examples for in-context learning are based on token embeddings. |
| Approach: | They propose an algorithm to select demonstration examples for in-context learning of a query set . they use gradients of the output taken in the input embedding space to estimate model outputs . |
| Outcome: | The proposed algorithm outperforms existing methods based on token embeddings by 11% . it scales up subset selection that would otherwise run full inference by 37.7 on models with 34 billion parameters . |