#73 - YASAMAN RAZEGHI & Prof. SAMEER SINGH - NLP benchmarks

Explainability

Explaining machine learning models is a complex task, often requiring intricate methods like LIME and SHAP. Sameer Singh highlights that these techniques, while valuable, come with caveats and are not universally applicable. He notes, "When we introduced LIME, we were very excited about it... but it produces comes with a bunch of caveats" 1. Yasaman Razeghi adds that simplifying explanations to yes-or-no questions can be misleading, as it may not capture the full picture 2.

   

Interpretability

Interpreting complex models presents significant challenges, as Sameer Singh explains. He emphasizes that while tools like LIME offer insights, they should be used cautiously due to their limitations 1. Tim Scarfe3. This highlights the difficulty in defining and measuring true model understanding.

   

Hybrid Models

Integrating symbolic systems into neural networks could enhance reasoning and explainability. Sameer Singh suggests that allowing models to use symbolic functions might improve generalization, as they wouldn't rely solely on memorization 4. He also discusses the potential for interactive testing frameworks to improve model robustness, though he cautions against making them too specific to a single model 5.