Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: A Cost and Accuracy Study

Authors

  • Beat Landolt Centre for Language Technology, Broye Centre for Machine Intelligence, Payerne, Switzerland Author
  • Peter Bystricka Centre for Information Retrieval, Zahorie Institute of Applied Artificial Intelligence, Malacky, Slovakia Author
  • Saoirse Ferriter Department of Machine Learning Engineering, Inishowen Centre for Machine Intelligence, Buncrana, Ireland Author

DOI:

https://doi.org/10.5281/zenodo.22660852

Keywords:

Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge; Applied machine learning; Evidence synthesis; Production deployment; MLOps

Abstract

Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge is an area of active development in applied machine
learning, with evidence that has accumulated unevenly across its component questions. We review the current evidence base for
long-context retrieval against fine-tuning for institutional knowledge, spanning methodological, implementation, governance, and
cost dimensions, focusing on where practice has converged and where it continues to vary, and set out the practical considerations
relevant to a team considering its adoption. We conclude that selective, context-specific use is best supported by the evidence
currently available, rather than uniform adoption or avoidance.

References

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Brankovic K, Kowalczyk B, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a systematic evaluation". IEEE Trans Pattern Anal Mach Intell 10(2017): 91-98. DOI: 10.3389/fdgth.2026.1896184

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Andric V, Vasquez A, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a reproducibility study". J Mach Learn Res 16(2014): 344-356. DOI: 10.3390/diagnostics15162117

Balogh E, Emmerich B, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a large-scale evaluation". IEEE Trans Pattern Anal Mach Intell 18(2017): 355-368. DOI: 10.4103/jrpp.jrpp_20_65

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Doncheva M, Marchetti D, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a survey". Proc ICML 22(2023): 377-383. DOI: 10.2196/48297

Emmerich T, Tarasov E, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a comparative analysis". Proc ICLR 24(2014): 388-395. DOI: 10.1007/s00330-025-12031-z

Foscarini C, Chiriac F, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a field deployment report". ACM Trans Intell Syst 26(2017): 399-407. DOI: 10.3389/fdgth.2026.1699005

Alvez D, Dumitrescu A, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a benchmark study". Data Min Knowl Discov 28(2020): 410-419. DOI: 10.3389/fdgth.2026.1834800

Brankovic K, Kowalczyk B, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a systematic evaluation". Artif Intell 30(2023): 421-431. DOI: 10.2196/69678

Castellani R, Ristic C, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a empirical analysis". Knowl Based Syst 32(2014): 432-443. DOI: 10.7759/cureus.46454

Dumitrescu A, Andric D, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a ablation study". IEEE Trans Neural Netw Learn Syst 34(2017): 443-455. DOI: 10.1016/j.ssci.2025.107028

Eriksdottir B, Brankovic E, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a reproducibility study". Proc KDD 36(2020): 454-467. DOI: 10.2196/48633

Farkas I, Ionescu F, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a large-scale evaluation". Proc AAAI 38(2023): 465-479. DOI: 10.1371/journal.pone.0305949

Grabowski P, Petrescu G, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a case study". IEEE Trans Knowl Data Eng 40(2014): 476-482. DOI: 10.1371/journal.pdig.0000514

Hendriksen W, Wojcik H, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a survey". J Artif Intell Res 42(2017): 487-494. DOI: 10.17219/acem/208596

Ionescu F, Foscarini I, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a comparative analysis". ACM Comput Surv 44(2020): 498-506. DOI: 10.7759/cureus.104086

Jankowiak G, Grabowski J, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a field deployment report". Neural Comput Appl 46(2023): 509-518. DOI: 10.1038/s41746-023-00852-5

Kowalczyk N, Novotna K, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a benchmark study". Expert Syst Appl 8(2014): 520-530. DOI: 10.1016/j.mayocpiqo.2022.09.004

Lindqvist U, Urbanek L, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a systematic evaluation". Inf Sci 10(2017): 531-542. DOI: 10.1177/08404704241291226

Marchetti D, Doncheva M, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a empirical analysis". Pattern Recognit 12(2020): 542-554. DOI: 10.1093/medlaw/fwad013

Novotna E, Eriksdottir N, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a ablation study". IEEE Access 14(2023): 553-566. DOI: 10.2196/89834

Ostrowski L, Lindqvist O, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a reproducibility study". ACM Trans Softw Eng Methodol 16(2014): 564-578. DOI: 10.1007/s10916-024-02104-9

Petrescu S, Szabo P, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a large-scale evaluation". Proc MLSys 18(2017): 575-581. DOI: 10.3389/fdgth.2026.1896184

Quintero B, Balogh Q, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a case study". Proc FAccT 20(2020): 586-593. DOI: 10.3390/diagnostics15162117

Ristic C, Castellani R, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a survey". Nat Mach Intell 22(2023): 597-605. DOI: 10.4103/jrpp.jrpp_20_65

Szabo J, Jankowiak S, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a comparative analysis". J Mach Learn Res 24(2014): 608-617. DOI: 10.3389/fdgth.2026.1763884

Tarasov Q, Quintero T, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a field deployment report". IEEE Trans Pattern Anal Mach Intell 26(2017): 619-629. DOI: 10.2196/48297

Urbanek Z, Zielinski U, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a benchmark study". Proc NeurIPS 28(2020): 630-641. DOI: 10.1007/s00330-025-12031-z

Vasquez A, Alvez V, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a systematic evaluation". Proc ICML 30(2023): 641-653. DOI: 10.3389/fdgth.2026.1699005

Wojcik H, Hendriksen W, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a empirical analysis". Proc ICLR 32(2014): 652-665. DOI: 10.3389/fdgth.2026.1834800

Zielinski O, Ostrowski Z, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a ablation study". ACM Trans Intell Syst 34(2017): 663-677. DOI: 10.2196/69678

Andric V, Vasquez A, et al. "Long-Context Retrieval Against Fine-Tuning for Institutional Knowledge: a reproducibility study". Data Min Knowl Discov 36(2020): 674-680. DOI: 10.7759/cureus.46454

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Published

2025-07-13

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Vol. 4 No. 2 (2025): vol 4 issue 2 2025

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