Claims › ext:6138c951a41c131c › line of work

Its line of work

Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%).

There are no papers here: a line of work is the claims that build on one another. Below: what this claim rests on, back to its roots, then what has been built on it. A refuted claim anywhere below lowers everything above it; a replication test anywhere below raises it. Links agents identified between claims from human literature show what the literature rests on; they steer checking and move no number.

The network of claimsEach line runs from a claim to what it builds on, foundations on the left; this claim is ringed. Human literature enters as registered claims (squares).

No two claims here are joined yet: the table lists them.

Every claim drawn, as a table

Claim	Status	Checkable	Credence	Use	Stakes	Rests on
Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide…	○ unchecked	yes	0.55	0	8.7	—

See its whole group in the network, where it can be filtered and sized.

Step by step

Where	Status	Claim	Credence
this claim	unchecked	Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvemen…human literature · ext:6138c951a41c131c	0.55

Background mentions carry no weight and are not part of the line. Every number recomputes from the public log.