Skip to main content
Springer Nature Link
Log in

Explainable Information Retrieval

  • Conference paper
  • First Online:
Advances in Information Retrieval (ECIR 2025)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15576))

Included in the following conference series:

  • 724 Accesses

  • 1 Citation

Abstract

This tutorial presents explainability of retrieval methods, an emerging area focused on fostering responsible and trustworthy deployment of machine learning systems in the context of information retrieval. As the field has rapidly evolved in the past 4–5 years, numerous approaches have been proposed that focus on different access modes, stakeholders, and model development stages. This tutorial aims to introduce IR-centric notions, classification, and evaluation styles in explainable information retrieval (ExIR) while focusing on IR-specific tasks such as ranking, text classification, and learning-to-rank systems. We will extensively cover post-hoc methods, probing approaches, and recent advances in interpretability-by-design approaches. We will also discuss ExIR applications for different stakeholders, such as researchers, practitioners, and end-users, in contexts like web search, legal search, and high-stakes decision-making tasks. To facilitate practical understanding, we will provide a hands-on session on ExIR methods, reducing the entry barrier for students, researchers, and practitioners alike. Earlier version of this tutorial has been presented in SIGIR 2023 and FIRE 2023.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+
from $39.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
eBook
USD 129.00
Price excludes VAT (USA)
Softcover Book
USD 169.99
Price excludes VAT (USA)

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.

References

  1. Anand, A., Lyu, L., Idahl, M., Wang, Y., Wallat, J., Zhang, Z.: Explainable information retrieval: a survey (2022)

    Google Scholar 

  2. Belinkov, Y.: Probing classifiers: promises, shortcomings, and advances. Comput. Linguistics 1, 207–219 (2022)

    Google Scholar 

  3. Bondarenko, A., Fröbe, M., Reimer, J.H., Stein, B., Völske, M., Hagen, M.: Axiomatic retrieval experimentation with ir_axioms. In: Proceedings of SIGIR 2022, pp. 3131–3140 (2022)

    Google Scholar 

  4. Choi, J., Jung, E., Lim, S., Rhee, W.: Finding inverse document frequency information in BERT. ArXiv preprint (2022)

    Google Scholar 

  5. Cohen, D., O’Connor, B., Croft, W.B.: Understanding the representational power of neural retrieval models using nlp tasks. In: Proceedings of 2018 ACM ICTIR, p. 67–74 (2018)

    Google Scholar 

  6. Danilevsky, M., Dhanorkar, S., Li, Y., Popa, L., Qian, K., Xu, A.: Explainability for natural language processing. In: Proc. of SIGKDD 2021, pp. 4033–4034 (2021)

    Google Scholar 

  7. Dietz, L., Bast, H., Chatterjee, S., Dalton, J., Meij, E., de Vries, A.: Neuro-symbolic approaches for information retrieval. In: Advances in Information Retrieval: 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, pp. 324–330 (2023)

    Google Scholar 

  8. Dietz, L., Bast, H., Chatterjee, S., Dalton, J., Nie, J.Y., Nogueira, R.: Neuro-symbolic representations for information retrieval. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, pp. 3436–3439. Association for Computing Machinery, New York (2023)

    Google Scholar 

  9. Dunefsky, J., Chlenski, P., Nanda, N.: Transcoders find interpretable LLM feature circuits. In: The Thirty-eighth Annual Conference on Neural Information Processing Systems (2024). https://openreview.net/forum?id=J6zHcScAo0

  10. Fan, Y., Guo, J., Ma, X., Zhang, R., Lan, Y., Cheng, X.: A linguistic study on relevance modeling in information retrieval, pp. 1053–1064 (2021)

    Google Scholar 

  11. Fernando, Z.T., Singh, J., Anand, A.: A study on the interpretability of neural retrieval models using deepshap. In: Proceedings of SIGIR 2019, pp. 1005–1008 (2019)

    Google Scholar 

  12. Formal, T., Piwowarski, B., Clinchant, S.: A white box analysis of ColBERT. In: Hiemstra, D., Moens, M.-F., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds.) ECIR 2021. LNCS, vol. 12657, pp. 257–263. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72240-1_23

    Chapter  MATH  Google Scholar 

  13. Huang, X., Panwar, M., Goyal, N., Hahn, M.: Inversionview: a general-purpose method for reading information from neural activations. In: The Thirty-eighth Annual Conference on Neural Information Processing Systems (2024). https://openreview.net/forum?id=clDGHpx2la

  14. Lei, T., Barzilay, R., Jaakkola, T.: Rationalizing neural predictions. In: Proceedings of EMNLP 2016, Austin, Texas, pp. 107–117 (2016)

    Google Scholar 

  15. Leonhardt, J., Rudra, K., Anand, A.: Extractive explanations for interpretable text ranking. ACM Trans. Inform. Syst. (2021)

    Google Scholar 

  16. Lucchese, C., Nardini, F.M., Orlando, S., Perego, R., Veneri, A.: ILMART: interpretable ranking with constrained lambdamart. In: Proceedings of SIGIR 2022, pp. 2255–2259 (2022)

    Google Scholar 

  17. Lundberg, S.M., Lee, S.: A unified approach to interpreting model predictions. In: Proceedings of NIPS 2017, pp. 4765–4774 (2017)

    Google Scholar 

  18. Lyu, L., Anand, A.: Listwise explanations for ranking models using multiple explainers. In: Advances in Information Retrieval: 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, pp. 653–668. Springer (2023). https://doi.org/10.1007/978-3-031-28244-7_41

  19. MacAvaney, S., Feldman, S., Goharian, N., Downey, D., Cohan, A.: ABNIRML: Analyzing the Behavior of Neural IR Models. ArXiv preprint (2020)

    Google Scholar 

  20. Polley, S.: Towards explainable search in legal text. In: Hagen, M., et al. (eds.) ECIR 2022. LNCS, vol. 13186, pp. 528–536. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99739-7_65

    Chapter  MATH  Google Scholar 

  21. Polley, S., Janki, A., Thiel, M., Hoebel-Mueller, J., Nuernberger, A.: Exdocs: evidence based explainable document search. In: Proceedings of SIGIR Workshop on Causality in Search and Recommendation 2021 (2021)

    Google Scholar 

  22. Purpura, A., Buchner, K., Silvello, G., Susto, G.A.: Neural feature selection for learning to rank. In: Hiemstra, D., Moens, M.-F., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds.) ECIR 2021. LNCS, vol. 12657, pp. 342–349. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72240-1_34

    Chapter  Google Scholar 

  23. Qiao, Y., Xiong, C., Liu, Z., Liu, Z.: Understanding the behaviors of bert in ranking. ArXiv preprint (2019)

    Google Scholar 

  24. Rahimi, R., Kim, Y., Zamani, H., Allan, J.: Explaining documents’ relevance to search queries. ArXiv preprint (2021)

    Google Scholar 

  25. Rau, D., Kamps, J.: The role of complex nlp in transformers for text ranking. In: Proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval, ICTIR 2022, pp. 153–160. Association for Computing Machinery (2022)

    Google Scholar 

  26. Ribeiro, M.T., Singh, S., Guestrin, C.: “why should i trust you?": explaining the predictions of any classifier. In: Proceedings of SIGKDD 2016, p. 1135–1144 (2016)

    Google Scholar 

  27. Roy, R.S., Anand, A.: Question answering for the curated web: tasks and methods in qa over knowledge bases and text collections. Synth. Lect. Synth. Lect. Inform. Concepts Retrieval Serv. 13(4), 1–194 (2021)

    MATH  Google Scholar 

  28. Saha, S., Majumdar, D., Mitra, M.: Explainability of text processing and retrieval methods: a critical survey (2022)

    Google Scholar 

  29. Sen, P., Ganguly, D., Verma, M., Jones, G.J.F.: The curious case of IR explainability: explaining document scores within and across ranking models. In: Proceedings of SIGIR 2020, pp. 2069–2072 (2020)

    Google Scholar 

  30. Singh, J., Anand, A.: EXS: explainable search using local model agnostic interpretability. In: Proceedings of WSDM 2019, pp. 770–773 (2019)

    Google Scholar 

  31. Singh, J., Anand, A.: Model agnostic interpretability of rankers via intent modelling. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 618–628 (2020)

    Google Scholar 

  32. Singh, J., Khosla, M., Zhenye, W., Anand, A.: Extracting per query valid explanations for blackbox learning-to-rank models. In: Proceedings of ICTIR 2021, pp. 203–210 (2021)

    Google Scholar 

  33. Sudhi, V., Bhat, S.R., Rudat, M., Teucher, R.: Rag-ex: a generic framework for explaining retrieval augmented generation. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, pp. 2776–2780. Association for Computing Machinery, New York (2024). https://doi.org/10.1145/3626772.3657660

  34. Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: Proceedings of ICML 2017, pp. 3319–3328. Proceedings of Machine Learning Research (2017)

    Google Scholar 

  35. Verma, M., Ganguly, D.: LIRME: locally interpretable ranking model explanation. In: Proceedings of SIGIR 2019, pp. 1281–1284 (2019)

    Google Scholar 

  36. Völske, M., et al.: Towards axiomatic explanations for neural ranking models. In: Proceedings of ICTIR 2021, pp. 13–22 (2021)

    Google Scholar 

  37. Wallat, J., Beringer, F., Anand, A., Anand, A.: Probing bert for ranking abilities. In: Advances in Information Retrieval: 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland. pp. 255–273. Springer (2023). https://doi.org/10.1007/978-3-031-28238-6_17

  38. Xu, R., Qi, Z., Guo, Z., Wang, C., Wang, H., Zhang, Y., Xu, W.: Knowledge conflicts for LLMs: A survey. In: Al-Onaizan, Y., Bansal, M., Chen, Y.N. (eds.) Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. pp. 8541–8565. Association for Computational Linguistics, Miami, Florida, USA (Nov 2024). https://doi.org/10.18653/v1/2024.emnlp-main.486, https://aclanthology.org/2024.emnlp-main.486/

  39. Yu, P., Rahimi, R., Allan, J.: Towards explainable search results: A listwise explanation generator. In: Proceedings of SIGIR 2022, pp. 669–680 (2022)

    Google Scholar 

  40. Zhang, R., Guo, J., Fan, Y., Lan, Y., Cheng, X.: Query understanding via intent description generation. In: Proceedings of CIKM 2020, pp. 1823–1832 (2020)

    Google Scholar 

  41. Zhang, Y.: Tutorial on explainable recommendation and search. In: Proceedings of ICTIR 2019. pp. 255–256 (2019)

    Google Scholar 

  42. Zhang, Y., Mao, J., Ai, Q.: Sigir 2019 tutorial on explainable recommendation and search. In: Proc. of SIGIR 2019, pp. 1417–1418 (2019)

    Google Scholar 

  43. Zhang, Y., Mao, J., Ai, Q.: Www’19 tutorial on explainable recommendation and search. In: Proceedings of WWW 2019, pp. 1330–1331 (2019)

    Google Scholar 

  44. Zhang, Z., Rudra, K., Anand, A.: Explain and predict, and then predict again. In: WSDM 2021, Israel, 8-12 March 2021, pp. 418–426. ACM (2021)

    Google Scholar 

  45. Zhang, Z., Rudra, K., Anand, A.: Faxplainac: a fact-checking tool based on explainable models with human correction in the loop. In: Proceedings of the 30th ACM CIKM, pp. 4823–4827 (2021)

    Google Scholar 

  46. Zhang, Z., Setty, V., Anand, A.: Sparcassist: a model risk assessment assistant based on sparse generated counterfactuals. In: Proceedings of SIGIR, pp. 3219–3223 (2022)

    Google Scholar 

  47. Zhuang, H., et al.: Interpretable ranking with generalized additive models. In: Proceedings of WSDM 2021, pp. 499–507 (2021)

    Google Scholar 

Download references

Acknowledgments

Sourav Saha is supported by TCS RSP PhD fellowship.

Author information

Authors and Affiliations

  1. Delft Institute of Technology, Delft, The Netherlands

    Avishek Anand & V. Venktesh

  2. Indian Statistical Institute, Kolkata, India

    Sourav Saha

Authors
  1. Avishek Anand
    View author publications

    Search author on:PubMed Google Scholar

  2. Sourav Saha
    View author publications

    Search author on:PubMed Google Scholar

  3. V. Venktesh
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Sourav Saha .

Editor information

Editors and Affiliations

  1. Spotify, Amsterdam, The Netherlands

    Claudia Hauff

  2. University of Glasgow, Glasgow, UK

    Craig Macdonald

  3. University of Klagenfurt, Klagenfurt, Austria

    Dietmar Jannach

  4. Amazon, London, UK

    Gabriella Kazai

  5. ISTI-CNR, Pisa, Italy

    Franco Maria Nardini

  6. IMT Lucca, Lucca, Italy

    Fabio Pinelli

  7. Sapienza University of Rome, Rome, Italy

    Fabrizio Silvestri

  8. University of Pisa, Pisa, Italy

    Nicola Tonellotto

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Anand, A., Saha, S., Venktesh, V. (2025). Explainable Information Retrieval. In: Hauff, C., et al. Advances in Information Retrieval. ECIR 2025. Lecture Notes in Computer Science, vol 15576. Springer, Cham. https://doi.org/10.1007/978-3-031-88720-8_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-88720-8_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-88719-2

  • Online ISBN: 978-3-031-88720-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Keywords

Publish with us

Policies and ethics