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Its line of work

We also find that existing methods of prompt tuning cannot handle hard sequence labeling tasks, indicating a lack of universality.

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).
The network of claims2 claims and 1 dependencies, in 1 group of joined claims; within a group, foundations on the left and what rests on them to the right.2 claims, 1 step deep, Computer ScienceWe also find that existing methods of prompt tuning cannot handle hard sequence labeling tasks, indicating a lack of un… refutes More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with sca… (identified in the literature)More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with sca…: unchecked, credence 0.55, stakes 11.5More remarkably…We also find that existing methods of prompt tuning cannot handle hard sequence labeling tasks, indicating a lack of un…: unchecked, credence 0.55, stakes 9.7We also find that…

● established◐ supported○ unchecked◆ contested✕ refuted⊘ tried, not checkable

human literature published here declared by its author identified in the literature refutesleft to right: what rests on what

size: stakes, by area; the largest here 11.5 the claim it is drawn around

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Every claim drawn, as a table
ClaimStatusCheckableCredenceUseStakesRests on
More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with sca…○ uncheckedyes0.55011.5—
We also find that existing methods of prompt tuning cannot handle hard sequence labeling tasks, indicating a lack of un…○ uncheckedyes0.5509.7More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with sca…

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Step by step

WhereStatusClaimCredence
1 step belowuncheckedMore remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with scale: as models exceed billions of paramet…this claim refutes it, as the citing paper says · human literature · ext:02b564306a95c50b0.55
this claimuncheckedWe also find that existing methods of prompt tuning cannot handle hard sequence labeling tasks, indicating a lack of universality.human literature · ext:c586a4e302c74e870.55

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