Efficient Shapley Values Estimation by Amortization for Text Classification

Understand

Despite the popularity of Shapley Values in explaining neural text classification models, computing them is prohibitive for large pretrained models due to a large number of model evaluations.

• In practice, Shapley Values are often estimated with a small number of stochastic model evaluations.
• However, we show that the estimated Shapley Values are sensitive to random seed choices -- the top-ranked features often have little overlap across different seeds, especially on examples with longer input texts.
• This can only be mitigated by aggregating thousands of model evaluations, which on the other hand, induces substantial computational overheads.