Fine-tuning 7B code LLMs on a custom multi-file DSL dataset achieves structural fidelity of 1.00, high exact-match accuracy, and practical utility validated by expert survey and execution checks.
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3 Pith papers cite this work, alongside 655 external citations. Polarity classification is still indexing.
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Multi-agent LLMs with human verification can generate formal representations of GDPR provisions, but structured oversight is required to handle legal nuances effectively.
Ishigaki-IDS is a verifier-aware LLM for generating validator-passing IDS files in BIM, reaching IDSAuditPass scores of 0.651-0.753 on a 166-case benchmark and cutting practitioner work time by 54.7%.
citing papers explorer
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Leveraging LLMs for Multi-File DSL Code Generation: An Industrial Case Study
Fine-tuning 7B code LLMs on a custom multi-file DSL dataset achieves structural fidelity of 1.00, high exact-match accuracy, and practical utility validated by expert survey and execution checks.
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GDPR Auto-Formalization with AI Agents and Human Verification
Multi-agent LLMs with human verification can generate formal representations of GDPR provisions, but structured oversight is required to handle legal nuances effectively.
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Ishigaki-IDS: An Open-Weight Verifier-Aware Model for Information Delivery Specification Drafting in Building Information Modeling
Ishigaki-IDS is a verifier-aware LLM for generating validator-passing IDS files in BIM, reaching IDSAuditPass scores of 0.651-0.753 on a 166-case benchmark and cutting practitioner work time by 54.7%.