Papers by James Wendt
Selective Labeling: How to Radically Lower Data-Labeling Costs for Document Extraction Models (2023.emnlp-main)
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| Challenge: | a key bottleneck in developing automatic extraction models for visually rich documents is the cost of acquiring labeled documents. |
| Approach: | They propose selective labeling to provide "yes/no" labels for candidate extractions predicted by a model trained on partially labeled documents. |
| Outcome: | The proposed method reduces the cost of acquiring labeled data by 10 with a negligible loss in accuracy. |
Enhancing Incremental Summarization with Structured Representations (2024.findings-emnlp)
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| Challenge: | Large language models struggle with processing extensive input contexts, leading to redundancy or incoherency. |
| Approach: | They propose a chain-of-key update based on JSON structured memory representations to improve summarization performance by 40% and 14% on two public datasets. |
| Outcome: | The proposed method improves summarization performance by 40% and 14% on two datasets. |