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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. COMMENTARY · dev.to — LLM tag English(EN) · · [2 sources]

    Stop Using JSON Mode for Structured Output. XML Tags Win 4 of 5 Cases.

    Developers are advised to use structured XML tags instead of JSON mode for LLM outputs, as XML offers better resilience against model migrations and streaming issues. This approach provides a more robust contract between the prompt and the model, ensuring consistency in data extraction and formatting. The author suggests a specific XML skeleton structure for prompts, detailing sections for role, format, refusal policies, and examples, which can be more reliable than prose-based instructions. AI

    Stop Using JSON Mode for Structured Output. XML Tags Win 4 of 5 Cases.

    IMPACT Adopting XML tags for LLM outputs can improve data extraction reliability and streaming performance, leading to more robust AI applications.

  2. RESEARCH · arXiv cs.CL English(EN) · · [2 sources]

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

    Researchers have introduced Ishigaki-IDS-Bench, a new benchmark designed to evaluate the capability of large language models (LLMs) in generating Information Delivery Specification (IDS) XML from Building Information Modeling (BIM) requirements. The benchmark includes 166 expert-verified examples across various construction domains and languages, along with gold IDS files for comparison. Initial evaluations show that while LLMs can partially express information requirements, they struggle to consistently generate XML that adheres to IDS standards and IFC vocabulary constraints, with the best model achieving only 65.6% content agreement. AI

    IMPACT This benchmark will help advance LLM capabilities in generating domain-specific, standardized structured data, crucial for industries like construction.