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Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements

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arXiv:2605. 22079v1 Announce Type: new Abstract: Large language models (LLMs) are widely used to generate structured outputs such as JSON, SQL, and code, yet public resources remain limited for evaluating generation that must simultaneously satisfy industry-standard XML and domain vocabulary constraints.

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Authors: Ryo Kanazawa, Koyo Hidaka, Teppei Miyamoto, Takayuki Kato, Tomoki Ando, Chenguang Wang, Dayuan Jiang, Naofumi Fujita, Shuhei Saitoh, Atomu Kondo, Koki Arakawa, Daiho Nishioka
Released: 2026-05-22 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, under, eval, source, rate, trained, language, model. Source: arxiv cs.CL (NLP).
arXiv: 2605.22079

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