Papers by Stefano Soatto

2 papers
DocKD: Knowledge Distillation from LLMs for Open-World Document Understanding Models (2024.emnlp-main)

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Challenge: Existing methods for visual document understanding are limited by training on a small-scale, curated document dataset, compromising generalizability of VDU models to diverse documents.
Approach: They propose a framework that integrates external document knowledge into the data generation process.
Outcome: The proposed framework produces high-quality annotations and surpasses direct knowledge distillation approach.
Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates (2021.acl-long)

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Challenge: Using negative flips, we quantify, reduce and analyze regression errors in deep neural networks.
Approach: They propose to quantify, reduce and analyze regression errors in NLP models by negative flips.
Outcome: The proposed model update regression has a prevalent presence across tasks in the GLUE benchmark.

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