# Explainable Information Retrieval: A Survey

arXiv: https://arxiv.org/abs/2211.02405 · HTML: https://mlsanity.com/paper/2211.02405/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2022.

## Built on by (most cited first, 15 of 15)

- [Nonet at SemEval-2023 Task 6: Methodologies for Legal Evaluation](https://mlsanity.com/paper/2310.11049/) (2023)
- [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)
- [Temporal Blind Spots in Large Language Models](https://mlsanity.com/paper/2401.12078/) (2024)
- [Explain like I am BM25: Interpreting a Dense Model's Ranked-List with a Sparse Approximation](https://mlsanity.com/paper/2304.12631/) (2023)
- [EXPLORA: Efficient Exemplar Subset Selection for Complex Reasoning](https://mlsanity.com/paper/2411.03877/) (2024)
- [RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank](https://mlsanity.com/paper/2405.01848/) (2024)
- [Hierarchical Semantic Retrieval with Cobweb](https://mlsanity.com/paper/2510.02539/) (2025)
- [Optimizing Retrieval for RAG via Reinforcement Learning](https://mlsanity.com/paper/2510.24652/) (2025)
- [Explainability of Text Processing and Retrieval Methods: A Survey](https://mlsanity.com/paper/2212.07126/) (2022)
- [Assistive AI for Augmenting Human Decision-making](https://mlsanity.com/paper/2410.14353/) (2024)
- [DISCO: DISCovering Overfittings as Causal Rules for Text Classification Models](https://mlsanity.com/paper/2411.04649/) (2024)
- [CREST: Improving Interpretability and Effectiveness of Troubleshooting at Ericsson through Criterion-Specific Trouble Report Retrieval](https://mlsanity.com/paper/2511.17417/) (2025)
- [LLMs as Assessors: Right for the Right Reason?](https://mlsanity.com/paper/2601.08919/) (2026)
- [Towards Explaining Query Expansion Performance in Information Retrieval](https://mlsanity.com/paper/2610.09724/) (2026)

## Abstract (arXiv)

Explainable information retrieval is an emerging research area aiming to make transparent and trustworthy information retrieval systems. Given the increasing use of complex machine learning models in search systems, explainability is essential in building and auditing responsible information retrieval models. This survey fills a vital gap in the otherwise topically diverse literature of explainable information retrieval. It categorizes and discusses recent explainability methods developed for different application domains in information retrieval, providing a common framework and unifying perspectives. In addition, it reflects on the common concern of evaluating explanations and highlights open challenges and opportunities.