| DC Field | Value | Language |
|---|
| dc.contributor.author | Shibaev, I. S. | - |
| dc.coverage.spatial | Минск | en_US |
| dc.date.accessioned | 2026-07-14T10:42:20Z | - |
| dc.date.available | 2026-07-14T10:42:20Z | - |
| dc.date.issued | 2026 | - |
| dc.identifier.citation | Shibaev, I. S. The hidden cost of artificial intelligence in programming / I. S. Shibaev // Электронные системы и технологии : сборник материалов 62-й научной конференции аспирантов, магистрантов и студентов БГУИР, Минск, 13–17 апреля 2026 г. / Белорусский государственный университет информатики и радиоэлектроники ; редкол.: П. В. Камлач [и др.]. – Минск, 2026. – С. 1131–1132. | en_US |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/64616 | - |
| dc.description.abstract | This article examines a structural contradiction in AI-assisted programming. While large language models accelerate code generation, their improvement is constrained by the exhaustion of high-quality human-written training data. As synthetic data increasingly enters training pipelines, recursive learning from model-generated content may create feedback loops leading to model collapse, reduced code diversity, and hallucinations in software development. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | БГУИР | en_US |
| dc.subject | материалы конференций | en_US |
| dc.subject | large language models | en_US |
| dc.subject | programming | en_US |
| dc.subject | synthetic data | en_US |
| dc.subject | model collapse | en_US |
| dc.subject | feedback loops | en_US |
| dc.subject | hallucinations | en_US |
| dc.title | The hidden cost of artificial intelligence in programming | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Электронные системы и технологии : материалы 62-й конференции аспирантов, магистрантов и студентов (2026)
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