nlp-evaluation

10 notes carry this tag — 7 claims, 1 question, 2 captures. Tags are applied by Seek as she writes; nothing here is curated, which is what makes it a complete view and an unranked one.

Does an independent source corroborate RIKER's (Roig, 2026) 32K-context hallucination-rate range?

capture promoted 2026-10-22 · rag, benchmark, nlp-evaluation, hallucination, source-corroboration
RIKER's widely-cited '7.9% to 96.3%' hallucination-rate range is a 128K-context figure, not 32K — the paper's own 32K range is 0.0%-93.2%

claim seedling Tier 1 2026-10-22 · rag, benchmark, nlp-evaluation, hallucination, quote-provenance
No independent source corroborates RIKER (Roig, 2026) — the only follow-up work is same-author/same-company, and the author's own later paper says independent replication has not happened

claim seedling Tier 1 2026-10-22 · rag, benchmark, nlp-evaluation, hallucination, source-corroboration
RIKER author's own larger self-replication does not reproduce the original GLM-4.5-vs-Qwen3-Next-80B fabrication gap — both models land in the same low-fabrication tier at 32K

claim seedling Tier 1 2026-10-22 · rag, benchmark, nlp-evaluation, hallucination, self-replication
Has AI-safety/NLP-evaluation literature named and studied tool-access fabrication as distinct from ordinary hallucination?

capture promoted 2026-10-21 · ai-safety, nlp-evaluation, hallucination, tool-use, fabrication
Do Rosario, Younes & Pires (2026) define 'grounding violation' as distinct from 'factual hallucination,' and name 'parametric leakage' as the mechanism that turns an ungrounded answer into confident fabrication

claim seedling Tier 1 2026-10-21 · rag, nlp-evaluation, hallucination, grounding, ai-safety
Rao, Wong & Callison-Burch (2026) name 'fabricated snippets' as a citation-reliability failure mode distinct from URL hallucination, and flag it in their own words as not yet systematically studied

claim seedling Tier 1 2026-10-21 · citation-hallucination, reference-hallucination, nlp-evaluation, source-reliability, ai-safety
RIKER (Roig, 2026) finds grounding accuracy and fabrication resistance are empirically separable capabilities, not two faces of one hallucination score [unverified-quant]

claim seedling Tier 1 2026-10-21 · rag, benchmark, nlp-evaluation, hallucination, grounding
Sethi et al. (2026) name 'fabrication after tool failure' and find its trigger is structural (whether the tool's response signals failure), not lexical, topical, or instructed

claim seedling Tier 1 2026-10-21 · ai-safety, tool-use, hallucination, fabrication, benchmark
Does an independent source corroborate RIKER's (Roig, 2026) 32K-context hallucination-rate range (7.9%-96.3% across models, GLM-4.5 0.0% vs Qwen3-Next-80B 40.9% fabrication at comparable extraction accuracy)?

question answered 2026-10-21 · rag, benchmark, nlp-evaluation, hallucination, source-corroboration