Paper Citation Record · LEDGER

On the Robustness of Generative Information Retrieval Models

As of 15 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2412.18768.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.18768 v1

Coverage vector

measured 57 of 57 reference resolution

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measured 58 of 58 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Reference resolution

57 of 57 outbound references displayed

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External citation measurements

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Outbound references

Observation f3115348-95ad-4eea-a204-5234fd3f608b · outbound

This paper cites Advances in Neural Information Processing Systems 35, 31668–31683 (2022).

On the Robustness of Generative Information Retrieval Models Advances in Neural Information Processing Systems 35, 31668–31683 (2022)

Reference 1

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This paper cites In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Informatio n Retrieval, pp.

On the Robustness of Generative Information Retrieval Models In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Informatio n Retrieval, pp

Reference 2

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This paper cites In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp.

On the Robustness of Generative Information Retrieval Models In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp

Reference 3

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This paper cites , Cheng, X.: A uniﬁed generative retriever for knowledge-intensive lan guage tasks via prompt learning.

On the Robustness of Generative Information Retrieval Models , Cheng, X.: A uniﬁed generative retriever for knowledge-intensive lan guage tasks via prompt learning

Reference 4

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This paper cites In: Proceedings of the 40th Inter- national ACM SIGIR Conference on Research and Development i n Infor- mation Retrieval, pp.

On the Robustness of Generative Information Retrieval Models In: Proceedings of the 40th Inter- national ACM SIGIR Conference on Research and Development i n Infor- mation Retrieval, pp

Reference 5

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This paper cites In: Proceedings of the Thirty- First Interna- tional Joint Conference on Artiﬁcial Intelligence, IJCAI, pp.

On the Robustness of Generative Information Retrieval Models In: Proceedings of the Thirty- First Interna- tional Joint Conference on Artiﬁcial Intelligence, IJCAI, pp

Reference 6

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This paper cites In: International Conference on Learning Represe ntations (2020).

On the Robustness of Generative Information Retrieval Models In: International Conference on Learning Represe ntations (2020)

Reference 7

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This paper cites In: Proceedings of NAACL-HLT, pp.

On the Robustness of Generative Information Retrieval Models In: Proceedings of NAACL-HLT, pp

Reference 8

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This paper cites I n: International Conference on Learning Representations (2018).

On the Robustness of Generative Information Retrieval Models I n: International Conference on Learning Representations (2018)

Reference 9

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This paper cites In: LREC 2018 (2018).

On the Robustness of Generative Information Retrieval Models In: LREC 2018 (2018)

Reference 10

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This paper cites In: Proceedings of the 57th An nual Meeting of the Association for Computational Linguistics, pp.

On the Robustness of Generative Information Retrieval Models In: Proceedings of the 57th An nual Meeting of the Association for Computational Linguistics, pp

Reference 11

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This paper cites In: Proceedings of the 2021 Conference on Empirica l Methods in Natural Language Processing (2021) On the Robustness of Generative Information Retrieval Mode ls 15.

On the Robustness of Generative Information Retrieval Models In: Proceedings of the 2021 Conference on Empirica l Methods in Natural Language Processing (2021) On the Robustness of Generative Information Retrieval Mode ls 15

Reference 12

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This paper cites ACM T ransactions on Information Systems 40(4), 1–42 (2022).

On the Robustness of Generative Information Retrieval Models ACM T ransactions on Information Systems 40(4), 1–42 (2022)

Reference 13

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Observation e8731075-fdab-48d0-94fe-e7c474c4ef52 · outbound

This paper cites Semantic Web 9(4), 459–479 (2018).

On the Robustness of Generative Information Retrieval Models Semantic Web 9(4), 459–479 (2018)

Reference 14

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This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

On the Robustness of Generative Information Retrieval Models A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 15

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This paper cites In: Pro- ceedings of the 58th Annual Meeting of the Association for Co mputational Linguistics, pp.

On the Robustness of Generative Information Retrieval Models In: Pro- ceedings of the 58th Annual Meeting of the Association for Co mputational Linguistics, pp

Reference 16

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This paper cites In: Proceedings of the 2011 conference on e mpirical methods in natural language processing, pp.

On the Robustness of Generative Information Retrieval Models In: Proceedings of the 2011 conference on e mpirical methods in natural language processing, pp

Reference 17

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On the Robustness of Generative Information Retrieval Models In: ACL, pp

Reference 18

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This paper cites In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.

On the Robustness of Generative Information Retrieval Models In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp

Reference 19

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This paper cites Proceeding s of the 43rd In- ternational ACM SIGIR Conference on Research and Developme nt in In- formation Retrieval pp.

On the Robustness of Generative Information Retrieval Models Proceeding s of the 43rd In- ternational ACM SIGIR Conference on Research and Developme nt in In- formation Retrieval pp

Reference 20

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This paper cites Transactions of the Association for Computational Linguistics 7, 453–466 (2019).

On the Robustness of Generative Information Retrieval Models Transactions of the Association for Computational Linguistics 7, 453–466 (2019)

Reference 21

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This paper cites In: Proceedings of the 21st Conf erence on Com- putational Natural Language Learning (CoNLL 2017), pp.

On the Robustness of Generative Information Retrieval Models In: Proceedings of the 21st Conf erence on Com- putational Natural Language Learning (CoNLL 2017), pp

Reference 22

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This paper cites In: Proceedings of the 58th Annual Meeting of the Associatio n for Compu- tational Linguistics, pp.

On the Robustness of Generative Information Retrieval Models In: Proceedings of the 58th Annual Meeting of the Associatio n for Compu- tational Linguistics, pp

Reference 23

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On the Robustness of Generative Information Retrieval Models Embedding-based Zero-shot Retrieval through Query Generation

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On the Robustness of Generative Information Retrieval Models In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp

Reference 25

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This paper cites In: Proceedings of the 18th ACM International Conference on Web Search and Data Mining (2025).

On the Robustness of Generative Information Retrieval Models In: Proceedings of the 18th ACM International Conference on Web Search and Data Mining (2025)

Reference 26

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On the Robustness of Generative Information Retrieval Models In: CIKM, p

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On the Robustness of Generative Information Retrieval Models In: SIGIR, p

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This paper cites In: Proceedings of the AAAI Conference on Ar tiﬁcial In- telligence (2025).

On the Robustness of Generative Information Retrieval Models In: Proceedings of the AAAI Conference on Ar tiﬁcial In- telligence (2025)

Reference 29

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On the Robustness of Generative Information Retrieval Models In: SIGIR (2024)

Reference 30

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This paper cites Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective.

On the Robustness of Generative Information Retrieval Models Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective

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On the Robustness of Generative Information Retrieval Models In: AAAI , vol

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This paper cites In: Proceedings of the 31st ACM International Conference on Information & Kn owledge Management, pp.

On the Robustness of Generative Information Retrieval Models In: Proceedings of the 31st ACM International Conference on Information & Kn owledge Management, pp

Reference 33

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This paper cites In: Proceedi ngs of the 29th annual international ACM SIGIR conference on Research and d evelopment in information retrieval, pp.

On the Robustness of Generative Information Retrieval Models In: Proceedi ngs of the 29th annual international ACM SIGIR conference on Research and d evelopment in information retrieval, pp

Reference 34

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On the Robustness of Generative Information Retrieval Models ACM SIGIR Forum 55(1), 1–27 (2021)

Reference 35

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This paper cites In: Proceedings of the 2 016 Conference of the North American Chapter of the Association for Computati onal Linguis- tics: Human Language Technologies, pp.

On the Robustness of Generative Information Retrieval Models In: Proceedings of the 2 016 Conference of the North American Chapter of the Association for Computati onal Linguis- tics: Human Language Technologies, pp

Reference 36

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On the Robustness of Generative Information Retrieval Models Generative Retrieval as Dense Retrieval

Reference 37

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On the Robustness of Generative Information Retrieval Models In: Advances in Information Re- trieval: 44th European Conference on IR Research, ECIR 2022 , Stavanger, Norway, April 10–14, 2022, Proceedings, Part I, pp

Reference 38

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This paper cites , De Cao, N., Thorne, J., Jernite, Y., Karpukhin, V., Maillard, J., Plachouras, V ., Rocktäschel, T., Riedel, S.: KILT: A benchmark for knowledge intensive la nguage tasks.

On the Robustness of Generative Information Retrieval Models , De Cao, N., Thorne, J., Jernite, Y., Karpukhin, V., Maillard, J., Plachouras, V ., Rocktäschel, T., Riedel, S.: KILT: A benchmark for knowledge intensive la nguage tasks

Reference 39

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On the Robustness of Generative Information Retrieval Models In: SIGIR, pp

Reference 40

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On the Robustness of Generative Information Retrieval Models In: Pr oceedings of the 17th Annual International ACM SIGIR Conference on Research and Devel- opment in Information Retrieval, p

Reference 41

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This paper cites Foundations and Trends in Information Retr ieval 3(4), 333–389 (2009).

On the Robustness of Generative Information Retrieval Models Foundations and Trends in Information Retr ieval 3(4), 333–389 (2009)

Reference 42

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This paper cites Communications of the ACM 18(11), 613–620 (1975).

On the Robustness of Generative Information Retrieval Models Communications of the ACM 18(11), 613–620 (1975)

Reference 43

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This paper cites In: Proceedings of the 45th Inter- national ACM SIGIR Conference on Research and Development i n Infor- mation Retrieval, p.

On the Robustness of Generative Information Retrieval Models In: Proceedings of the 45th Inter- national ACM SIGIR Conference on Research and Development i n Infor- mation Retrieval, p

Reference 44

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This paper cites Transformer Memory as a Differentiable Search Index.

On the Robustness of Generative Information Retrieval Models Transformer Memory as a Differentiable Search Index

Reference 45

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This paper cites In: Thirty-ﬁfth Conference on Neural Information P rocessing Sys- tems Datasets and Benchmarks Track (2021).

On the Robustness of Generative Information Retrieval Models In: Thirty-ﬁfth Conference on Neural Information P rocessing Sys- tems Datasets and Benchmarks Track (2021)

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:34:28.378191Z

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Observation 72320c0c-f737-4132-99f4-8f66683debab · outbound

This paper cites In: Proc eedings of the 2018 Conference of the North American Chapter of the Associa tion for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp.

On the Robustness of Generative Information Retrieval Models In: Proc eedings of the 2018 Conference of the North American Chapter of the Associa tion for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:34:28.365388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 50fa8c7f-3464-436d-b047-83f6a65e2db0 · outbound

This paper cites A Neural Corpus Indexer for Document Retrieval.

On the Robustness of Generative Information Retrieval Models A Neural Corpus Indexer for Document Retrieval

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T04:34:28.069777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44d74cac-d4bf-4218-a38b-459c756a382e · outbound

This paper cites an unresolved cited work.

On the Robustness of Generative Information Retrieval Models Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-11T04:34:28.352479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f0bfe5da-4d26-4e00-a95b-32aae21b8d39 · outbound

This paper cites In: SIGIR 2024: 47th international ACM SIGIR Conference on Research and Dev elopment in Information Retrieval, pp.

On the Robustness of Generative Information Retrieval Models In: SIGIR 2024: 47th international ACM SIGIR Conference on Research and Dev elopment in Information Retrieval, pp

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:34:28.339806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b71dce24-207c-4b32-928e-cb84fec5db4a · outbound

This paper cites Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval.

On the Robustness of Generative Information Retrieval Models Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T04:34:28.082020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae9c83fc-6664-4be2-aae2-2dfe5180fcbd · outbound

This paper cites In: EMNLP, pp.

On the Robustness of Generative Information Retrieval Models In: EMNLP, pp

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:34:28.325654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fce037da-cfbe-4693-8e82-303fb9482e70 · outbound

This paper cites arXiv preprint arXiv:2210.15 212 (2022).

On the Robustness of Generative Information Retrieval Models arXiv preprint arXiv:2210.15 212 (2022)

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:34:28.311008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4d692253-6fc7-49f3-8e61-f13cbad10d40 · outbound

This paper cites Proceedings of the 44th Inter- national ACM SIGIR Conference on Research and Development i n Infor- mation Retrieval pp.

On the Robustness of Generative Information Retrieval Models Proceedings of the 44th Inter- national ACM SIGIR Conference on Research and Development i n Infor- mation Retrieval pp

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:34:28.296344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ecf5468b-88d2-4706-a59d-73a5c9d0dadf · outbound

This paper cites International Journal of Machine Learnin g and Cybernetics 1, 43–52 (2010).

On the Robustness of Generative Information Retrieval Models International Journal of Machine Learnin g and Cybernetics 1, 43–52 (2010)

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:34:28.281623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2ae36b60-1385-42b6-8059-4d5eec237b94 · outbound

This paper cites ACM Transactions on I nformation Systems 42(4), 1–60 (2024).

On the Robustness of Generative Information Retrieval Models ACM Transactions on I nformation Systems 42(4), 1–60 (2024)

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:34:28.267410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 03f67121-f3a2-4ea9-970a-9a7cf58f7cf8 · outbound

This paper cites In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp.

On the Robustness of Generative Information Retrieval Models In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:34:28.253085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation 3245b7cb-5929-443f-8010-3ba8943cab09 · inbound

Understanding and Debugging Failures in N-Gram-Based Generative Retrieval cites this paper.

Understanding and Debugging Failures in N-Gram-Based Generative Retrieval On the Robustness of Generative Information Retrieval Models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-07-03T23:19:03.271589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.