Beyond Accuracy: Behavioral Testing of NLP Models with Sameer Singh - #406
Published:Sep 3, 2020 19:10
•1 min read
•Practical AI
Analysis
This article summarizes a podcast episode featuring Sameer Singh, an assistant professor at UC Irvine, discussing his work on behavioral testing of NLP models. The core focus is on CheckLists, a task-agnostic methodology for evaluating NLP models, as presented in his ACL 2020 best paper. The conversation also touches upon understanding failure modes in deep learning, embodied AI, and Singh's work on the LIME paper. The article highlights the importance of going beyond simple accuracy metrics to assess the robustness and reliability of NLP systems.
Key Takeaways
- •The article introduces CheckLists, a methodology for testing NLP models.
- •The discussion covers the importance of understanding failure modes in deep learning.
- •The episode touches upon embodied AI and the LIME paper.
Reference
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