KAMEL
Knowledge Analysis with Multitoken Entities in Language Models
KAMEL comprises knowledge about 234 relations from Wikidata with a large training, validation, and test dataset.
How to run
$ npm i -g lettokenburn-cli$ ltb bench info kamel
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
Scores
1 sourced entryEvery number links to where it was published. Low-confidence rows are muted and need per-cell verification.
Reproduced by the community
All runsAbout
KAMEL comprises knowledge about 234 relations from Wikidata with a large training, validation, and test dataset. We make sure that all facts are also present in Wikipedia so that they have been seen during the pre-training procedure of the LMs we are probing. Most importantly we overcome the limitations of existing probing datasets by (1) having a larger variety of knowledge graph relations, (2) it contains single- and multi-token entities, (3) we use relations with literals, and (4) have alternative labels for entities. (5) Furthermore, we created an evaluation procedure for higher cardinality relations, which was missing in previous works, and (6) make sure that the dataset can be used for causal LMs.