Venkata S Govindarajan
2023
Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike Ways
Venkata S Govindarajan
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Juan Diego Rodriguez
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Kaj Bostrom
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Kyle Mahowald
Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning
2022
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.
Venelin Kovatchev
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Trina Chatterjee
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Venkata S Govindarajan
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Jifan Chen
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Eunsol Choi
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Gabriella Chronis
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Anubrata Das
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Katrin Erk
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Matthew Lease
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Junyi Jessy Li
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Yating Wu
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Kyle Mahowald
Proceedings of the First Workshop on Dynamic Adversarial Data Collection
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability. Here, we describe the approach of the team “longhorns” on Task 1 of the The First Workshop on Dynamic Adversarial Data Collection (DADC), which asked teams to manually fool a model on an Extractive Question Answering task. Our team finished first (pending validation), with a model error rate of 62%. We advocate for a systematic, linguistically informed approach to formulating adversarial questions, and we describe the results of our pilot experiments, as well as our official submission.
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Co-authors
- Kyle Mahowald 2
- Venelin Kovatchev 1
- Trina Chatterjee 1
- Jifan Chen 1
- Eunsol Choi 1
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