mSTEB is a new 200+ language speech and text benchmark showing that LLMs perform substantially worse on low-resource African and Americas/Oceania languages, especially in speech tasks.
SIB-200: A simple, inclusive, and big evaluation dataset for topic classification in 200+ languages and dialects,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
mSTEB: Massively Multilingual Evaluation of LLMs on Speech and Text Tasks
mSTEB is a new 200+ language speech and text benchmark showing that LLMs perform substantially worse on low-resource African and Americas/Oceania languages, especially in speech tasks.