# EXS: Explainable Search Using Local Model Agnostic Interpretability

arXiv: https://arxiv.org/abs/1809.03857 · HTML: https://mlsanity.com/paper/1809.03857/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2018.

## Built on by (most cited first, 18 of 18)

- [BERTnesia: Investigating the capture and forgetting of knowledge in BERT](https://mlsanity.com/paper/2106.02902/) (2021)
- [Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank](https://mlsanity.com/paper/2212.12722/) (2022)
- [Query Understanding in the Age of Large Language Models](https://mlsanity.com/paper/2306.16004/) (2023)
- [Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning](https://mlsanity.com/paper/2103.00370/) (2021)
- [FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn Correction in the Loop](https://mlsanity.com/paper/2110.10144/) (2021)
- [RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank](https://mlsanity.com/paper/2405.01848/) (2024)
- [ShaRP: Explaining Rankings and Preferences with Shapley Values](https://mlsanity.com/paper/2401.16744/) (2024)
- [Exposing Query Identification for Search Transparency](https://mlsanity.com/paper/2110.07701/) (2021)
- [ExaRanker: Explanation-Augmented Neural Ranker](https://mlsanity.com/paper/2301.10521/) (2023)
- [Probing Ranking LLMs: A Mechanistic Analysis for Information Retrieval](https://mlsanity.com/paper/2410.18527/) (2024)
- [SparCAssist: A Model Risk Assessment Assistant Based on Sparse Generated Counterfactuals](https://mlsanity.com/paper/2205.01588/) (2022)
- [Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths](https://mlsanity.com/paper/2203.02295/) (2022)
- [Explainability of Text Processing and Retrieval Methods: A Survey](https://mlsanity.com/paper/2212.07126/) (2022)
- [Harnessing PubMed User Query Logs for Post Hoc Explanations of Recommended Similar Articles](https://mlsanity.com/paper/2402.03484/) (2024)
- [Comparison of decision trees with Local Interpretable Model-Agnostic Explanations (LIME) technique and multi-linear regression for explaining support vector regression model in terms of root mean square error (RMSE) values](https://mlsanity.com/paper/2404.07046/) (2024)
- [Explaining Expert Search and Team Formation Systems with ExES](https://mlsanity.com/paper/2405.12881/) (2024)
- [XSearch: Explainable Code Search via Concept-to-Code Alignment](https://mlsanity.com/paper/2605.16046/) (2026)
- [Why This, Not That? Mining User Profiles for Pair-wise Counterfactuals](https://mlsanity.com/paper/2608.21662/) (2026)

## Abstract (arXiv)

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.