EXS: Explainable Search Using Local Model Agnostic Interpretability
This work addresses the need for explainability in neural rankers for users and developers in information retrieval, though it is incremental as it adapts an existing method to a specific domain.
The paper tackles the problem of understanding complex neural ranking models in information retrieval by introducing EXS, a search system that provides insights into query intent and document ranking decisions using an adapted version of LIME, effectively helping users interpret results and identify areas for improvement.
Retrieval models in information retrieval are used to rank documents for typically under-specified queries. Today machine learning is used to learn retrieval models from click logs and/or relevance judgments that maximizes an objective correlated with user satisfaction. As these models become increasingly powerful and sophisticated, they also become harder to understand. Consequently, it is hard for to identify artifacts in training, data specific biases and intents from a complex trained model like neural rankers even if trained purely on text features. EXS is a search system designed specifically to provide its users with insight into the following questions: `What is the intent of the query according to the ranker?', `Why is this document ranked higher than another?' and `Why is this document relevant to the query?'. EXS uses a version of a popular posthoc explanation method for classifiers -- LIME, adapted specifically to answer these questions. We show how such a system can effectively help a user understand the results of neural rankers and highlight areas of improvement.