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Explainable Information Retrieval: A Survey
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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.
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Cited by 3 Pith papers
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LLMs Encode Relevance as a Layer-Wise Cross-Lingual Signal
Large language models encode query-document relevance as a linearly decodable internal signal that strengthens in middle-to-late layers and, in several models, outperforms their own generated judgments.
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Decoding Dense Embeddings: Sparse Autoencoders for Interpreting and Discretizing Dense Retrieval
Dense retrieval embeddings can be decomposed into interpretable latent concepts that serve both as explanations and as efficient sparse indexing units for retrieval.
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Explainable Information Retrieval in the Audit Domain
A position paper proposing research directions and challenges for explainable information retrieval (XIR) in the audit domain, with no empirical results.
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