LetTokenBurnModern Mongolian Evaluation
Large language models (LLMs) excel in high-resource languages but face notable challenges in low-resource languages like Mongolian.
$ npm i -g lettokenburn-cli$ ltb bench info mm-eval
External harness. No Harbor adapter has been generated for this benchmark yet. `ltb bench info` prints the known links.
Runs happen on your machine with your own subscription; only the score summary is pushed. CLI docs
No sourced scores recorded yet.
Every number links to where it was published. Low-confidence rows are muted and need per-cell verification.
Large language models (LLMs) excel in high-resource languages but face notable challenges in low-resource languages like Mongolian. The release of MM-Eval, comprising 569 syntax, 677 semantics, 344 knowledge, and 250 reasoning tasks, offers valuable insights for advancing NLP and LLMs in low-resource languages like Mongolian.