Academic Journal
Comparative Analysis of Local Large Language Models for Ranking Higher Education Programmes Based on Applicant Digital Profiles.
| Τίτλος: | Comparative Analysis of Local Large Language Models for Ranking Higher Education Programmes Based on Applicant Digital Profiles. |
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| Συγγραφείς: | Sveshnikov, Artem, Nikitnikov, Yury, Shiltsyn, Maxim, Dedov, Denis, Obukhov, Artem |
| Πηγή: | Technologies (2227-7080); Jul2026, Vol. 14 Issue 7, p438, 37p |
| Θεματικοί όροι: | Language models, Information retrieval, Vocational guidance, Statistical accuracy |
| Περίληψη: | Choosing among closely related higher-education programmes requires interpretation of heterogeneous applicant data while preserving data confidentiality. This study compares ten locally executable large language model (LLM) configurations as semantic rankers, contextualises their performance against a hybrid term frequency–inverse document frequency (TF–IDF) cosine baseline, and evaluates robustness when relevant competencies are expressed indirectly. The main benchmark comprised 50 anonymised applicant profiles and 10 degree programmes; an additional processed-profile robustness set comprising 10 profiles was used to reduce direct lexical overlap with the programme catalogue. Rankings were evaluated using Accuracy@1, Accuracy@3, normalised discounted cumulative gain at 5 (NDCG@5) and mean reciprocal rank at 5 (MRR@5), while structured-output validity and inference time were assessed for the LLMs. Among the local LLMs, gpt-oss-20b-MXFP4 achieved the highest Accuracy@1 (0.76), whereas gemma-4-E4B-it-Q8_0 achieved the highest Accuracy@3 (0.92), and Ministral-3-14B-Reasoning-2512-Q4_K_M provided a favourable quality–latency balance. On the main benchmark, TF–IDF achieved Accuracy@1 = 0.88 and NDCG@5 = 0.9557, exceeding all LLM configurations and demonstrating a strong lexical signal. On the processed-profile robustness set, the Accuracy@1 of the TF–IDF baseline decreased to 0.60, whereas gemma achieved 0.90. The results provide initial evidence that several local LLM configurations can reproduce observed programme-selection patterns in a limited pilot benchmark. However, the findings should be interpreted as task-specific model-comparison results rather than as full validation of an autonomous career guidance system. [ABSTRACT FROM AUTHOR] |
| Copyright of Technologies (2227-7080) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Comparative Analysis of Local Large Language Models for Ranking Higher Education Programmes Based on Applicant Digital Profiles. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sveshnikov%2C+Artem%22">Sveshnikov, Artem</searchLink><br /><searchLink fieldCode="AR" term="%22Nikitnikov%2C+Yury%22">Nikitnikov, Yury</searchLink><br /><searchLink fieldCode="AR" term="%22Shiltsyn%2C+Maxim%22">Shiltsyn, Maxim</searchLink><br /><searchLink fieldCode="AR" term="%22Dedov%2C+Denis%22">Dedov, Denis</searchLink><br /><searchLink fieldCode="AR" term="%22Obukhov%2C+Artem%22">Obukhov, Artem</searchLink> – Name: TitleSource Label: Source Group: Src Data: Technologies (2227-7080); Jul2026, Vol. 14 Issue 7, p438, 37p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Vocational+guidance%22">Vocational guidance</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+accuracy%22">Statistical accuracy</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Choosing among closely related higher-education programmes requires interpretation of heterogeneous applicant data while preserving data confidentiality. This study compares ten locally executable large language model (LLM) configurations as semantic rankers, contextualises their performance against a hybrid term frequency–inverse document frequency (TF–IDF) cosine baseline, and evaluates robustness when relevant competencies are expressed indirectly. The main benchmark comprised 50 anonymised applicant profiles and 10 degree programmes; an additional processed-profile robustness set comprising 10 profiles was used to reduce direct lexical overlap with the programme catalogue. Rankings were evaluated using Accuracy@1, Accuracy@3, normalised discounted cumulative gain at 5 (NDCG@5) and mean reciprocal rank at 5 (MRR@5), while structured-output validity and inference time were assessed for the LLMs. Among the local LLMs, gpt-oss-20b-MXFP4 achieved the highest Accuracy@1 (0.76), whereas gemma-4-E4B-it-Q8_0 achieved the highest Accuracy@3 (0.92), and Ministral-3-14B-Reasoning-2512-Q4_K_M provided a favourable quality–latency balance. On the main benchmark, TF–IDF achieved Accuracy@1 = 0.88 and NDCG@5 = 0.9557, exceeding all LLM configurations and demonstrating a strong lexical signal. On the processed-profile robustness set, the Accuracy@1 of the TF–IDF baseline decreased to 0.60, whereas gemma achieved 0.90. The results provide initial evidence that several local LLM configurations can reproduce observed programme-selection patterns in a limited pilot benchmark. However, the findings should be interpreted as task-specific model-comparison results rather than as full validation of an autonomous career guidance system. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Technologies (2227-7080) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/technologies14070438 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 37 StartPage: 438 Subjects: – SubjectFull: Language models Type: general – SubjectFull: Information retrieval Type: general – SubjectFull: Vocational guidance Type: general – SubjectFull: Statistical accuracy Type: general Titles: – TitleFull: Comparative Analysis of Local Large Language Models for Ranking Higher Education Programmes Based on Applicant Digital Profiles. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sveshnikov, Artem – PersonEntity: Name: NameFull: Nikitnikov, Yury – PersonEntity: Name: NameFull: Shiltsyn, Maxim – PersonEntity: Name: NameFull: Dedov, Denis – PersonEntity: Name: NameFull: Obukhov, Artem IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22277080 Numbering: – Type: volume Value: 14 – Type: issue Value: 7 Titles: – TitleFull: Technologies (2227-7080) Type: main |
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