Claims › ext:635e650ddfbc9cb4 › line of work

Its line of work

Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%.

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 a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-…	○ unchecked	yes	0.55	0	8.0	—

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 a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA…human literature · ext:635e650ddfbc9cb4	0.55

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