Claims › ext:1d2cbada40dbe070 › line of work

Its line of work

Using an instruction tuning approach on an extensive dataset of radiology domain knowledge, Radiology-GPT demonstrates superior performance compared to general language models such as StableLM, Dolly and LLaMA.

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
Using an instruction tuning approach on an extensive dataset of radiology domain knowledge, Radiology-GPT demonstrates…	○ unchecked	yes	0.55	0	4.4	—

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

Step by step

Where	Status	Claim	Credence
this claim	unchecked	Using an instruction tuning approach on an extensive dataset of radiology domain knowledge, Radiology-GPT demonstrates superior performance compared to general…human literature · ext:1d2cbada40dbe070	0.55

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