Instruction Tuning Against Prompting for Domain Classification: A Cost-Matched Comparison

Authors

  • Birgitta Soderstrom Centre for Language Technology, Bohuslan Institute of Applied Artificial Intelligence, Uddevalla, Sweden Author
  • Andrea Sertic Department of Machine Learning Engineering, Baranja Centre for Machine Intelligence, Beli Manastir, Croatia Author
  • Jarmila Mihalik Department of Machine Learning Engineering, Zahorie Institute of Applied Artificial Intelligence, Malacky, Slovakia Author

DOI:

https://doi.org/10.5281/zenodo.22660631

Keywords:

Instruction Tuning Against Prompting for Domain Classification; Applied machine learning; Evidence synthesis; Production deployment; MLOps

Abstract

Instruction Tuning Against Prompting for Domain Classification 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 instruction
tuning against prompting for domain classification, 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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Doncheva M, Marchetti D, et al. "Instruction Tuning Against Prompting for Domain Classification: a survey". Proc ICML 22(2023): 377-383. DOI: 10.1038/s41746-023-00852-5

Emmerich T, Tarasov E, et al. "Instruction Tuning Against Prompting for Domain Classification: a comparative analysis". Proc ICLR 24(2014): 388-395. DOI: 10.1016/j.mayocpiqo.2022.09.004

Foscarini C, Chiriac F, et al. "Instruction Tuning Against Prompting for Domain Classification: a field deployment report". ACM Trans Intell Syst 26(2017): 399-407. DOI: 10.1177/08404704241291226

Alvez D, Dumitrescu A, et al. "Instruction Tuning Against Prompting for Domain Classification: a benchmark study". Data Min Knowl Discov 28(2020): 410-419. DOI: 10.1093/medlaw/fwad013

Brankovic K, Kowalczyk B, et al. "Instruction Tuning Against Prompting for Domain Classification: a systematic evaluation". Artif Intell 30(2023): 421-431. DOI: 10.2196/89834

Castellani R, Ristic C, et al. "Instruction Tuning Against Prompting for Domain Classification: a empirical analysis". Knowl Based Syst 32(2014): 432-443. DOI: 10.1007/s10916-024-02104-9

Dumitrescu A, Andric D, et al. "Instruction Tuning Against Prompting for Domain Classification: a ablation study". IEEE Trans Neural Netw Learn Syst 34(2017): 443-455. DOI: 10.3389/fdgth.2026.1896184

Eriksdottir B, Brankovic E, et al. "Instruction Tuning Against Prompting for Domain Classification: a reproducibility study". Proc KDD 36(2020): 454-467. DOI: 10.3390/diagnostics15162117

Farkas I, Ionescu F, et al. "Instruction Tuning Against Prompting for Domain Classification: a large-scale evaluation". Proc AAAI 38(2023): 465-479. DOI: 10.4103/jrpp.jrpp_20_65

Grabowski P, Petrescu G, et al. "Instruction Tuning Against Prompting for Domain Classification: a case study". IEEE Trans Knowl Data Eng 40(2014): 476-482. DOI: 10.3389/fdgth.2026.1763884

Hendriksen W, Wojcik H, et al. "Instruction Tuning Against Prompting for Domain Classification: a survey". J Artif Intell Res 42(2017): 487-494. DOI: 10.2196/48297

Ionescu F, Foscarini I, et al. "Instruction Tuning Against Prompting for Domain Classification: a comparative analysis". ACM Comput Surv 44(2020): 498-506. DOI: 10.1007/s00330-025-12031-z

Jankowiak G, Grabowski J, et al. "Instruction Tuning Against Prompting for Domain Classification: a field deployment report". Neural Comput Appl 46(2023): 509-518. DOI: 10.3389/fdgth.2026.1699005

Kowalczyk N, Novotna K, et al. "Instruction Tuning Against Prompting for Domain Classification: a benchmark study". Expert Syst Appl 8(2014): 520-530. DOI: 10.3389/fdgth.2026.1834800

Lindqvist U, Urbanek L, et al. "Instruction Tuning Against Prompting for Domain Classification: a systematic evaluation". Inf Sci 10(2017): 531-542. DOI: 10.2196/69678

Marchetti D, Doncheva M, et al. "Instruction Tuning Against Prompting for Domain Classification: a empirical analysis". Pattern Recognit 12(2020): 542-554. DOI: 10.7759/cureus.46454

Novotna E, Eriksdottir N, et al. "Instruction Tuning Against Prompting for Domain Classification: a ablation study". IEEE Access 14(2023): 553-566. DOI: 10.1016/j.ssci.2025.107028

Ostrowski L, Lindqvist O, et al. "Instruction Tuning Against Prompting for Domain Classification: a reproducibility study". ACM Trans Softw Eng Methodol 16(2014): 564-578. DOI: 10.2196/48633

Petrescu S, Szabo P, et al. "Instruction Tuning Against Prompting for Domain Classification: a large-scale evaluation". Proc MLSys 18(2017): 575-581. DOI: 10.1371/journal.pone.0305949

Quintero B, Balogh Q, et al. "Instruction Tuning Against Prompting for Domain Classification: a case study". Proc FAccT 20(2020): 586-593. DOI: 10.1371/journal.pdig.0000514

Ristic C, Castellani R, et al. "Instruction Tuning Against Prompting for Domain Classification: a survey". Nat Mach Intell 22(2023): 597-605. DOI: 10.17219/acem/208596

Szabo J, Jankowiak S, et al. "Instruction Tuning Against Prompting for Domain Classification: a comparative analysis". J Mach Learn Res 24(2014): 608-617. DOI: 10.7759/cureus.104086

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Urbanek Z, Zielinski U, et al. "Instruction Tuning Against Prompting for Domain Classification: a benchmark study". Proc NeurIPS 28(2020): 630-641. DOI: 10.1016/j.mayocpiqo.2022.09.004

Vasquez A, Alvez V, et al. "Instruction Tuning Against Prompting for Domain Classification: a systematic evaluation". Proc ICML 30(2023): 641-653. DOI: 10.1177/08404704241291226

Wojcik H, Hendriksen W, et al. "Instruction Tuning Against Prompting for Domain Classification: a empirical analysis". Proc ICLR 32(2014): 652-665. DOI: 10.1093/medlaw/fwad013

Zielinski O, Ostrowski Z, et al. "Instruction Tuning Against Prompting for Domain Classification: a ablation study". ACM Trans Intell Syst 34(2017): 663-677. DOI: 10.2196/89834

Andric V, Vasquez A, et al. "Instruction Tuning Against Prompting for Domain Classification: a reproducibility study". Data Min Knowl Discov 36(2020): 674-680. DOI: 10.1007/s10916-024-02104-9

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Published

2024-01-10

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Vol. 3 No. 1 (2024): vol 3 issue 1 2024

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Articles

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