Natural Language Code Retrieval for 1C:Enterprise: An Open Benchmark and Efficient Bi-Encoder
Natural language code retrieval is a rapidly evolving task in computer science. However, the 1C:Enterprise ecosystem combines Russian syntax with highly domain-specific terminology, for which open datasets and specialized models have been virtually non-existent. We present a comprehensive pipeline for 1C code retrieval: an open benchmark of 3,413 real-world, PII-scrubbed query-code pairs, a reproducible evaluation harness, and a specialized bi-encoder. To overcome scarce labeled data, we fine-tune on 784,057 synthetic triplets generated by google/gemma-4-26B-A4B-it from public code repositorie
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 53%Dataset of permissively licensed code released →
- PossiblePossibly related (embedding) · 51%Best GitHub Repos to Learn Claude Code in 2026: Top 10 Picks - Analytics Insight →
- PossiblePossibly related (embedding) · 50%DeepSWE: new benchmark looking at how well today's frontier models can actually write code [R] →
- FuzzySimilar title/name (fuzzy) · 59%jeinlee1991/chinese-llm-benchmark →
“Fuzzy title match (0.73): “Natural Language Code Retrieval for 1C:Enterprise: An Open B” ≈ “jeinlee1991/chinese-llm-benchmark””
- LinkedLinked via arxiv author · 85%Konstantin Chesnokov →
“Natural Language Code Retrieval for 1C:Enterprise: An Open Benchmark and Efficient Bi-Encoder”
- LinkedLinked via arxiv author · 85%Chingiz Mingazov →
“Natural Language Code Retrieval for 1C:Enterprise: An Open Benchmark and Efficient Bi-Encoder”