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Its line of work

Across five tasks and two LLMs, sampling from stable subsets selected by CondAcc and Datamodels improves average accuracy over sampling from the entire training set by 7.7% and 6.3%, respectively.

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 ScienceAcross five tasks and two LLMs, sampling from stable subsets selected by CondAcc and Datamodels improves average accura… takes its method from Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reach… (identified in the literature)Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reach…: unchecked, credence 0.55, stakes 15.0, reliance 1.0Here we show that…Across five tasks and two LLMs, sampling from stable subsets selected by CondAcc and Datamodels improves average accura…: unchecked, credence 0.55, stakes 4.0Across five tasks and…

● 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 15.0 the claim it is drawn around

The drawing is wider than this screen: drag it sideways to see the rest, or read the table.

Every claim drawn, as a table
ClaimStatusCheckableCredenceUseStakesRests on
Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reach…○ uncheckedyes0.55015.0—
Across five tasks and two LLMs, sampling from stable subsets selected by CondAcc and Datamodels improves average accura…○ uncheckedyes0.5504.0Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reach…

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

Step by step

WhereStatusClaimCredence
1 step belowuncheckedHere we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-…this claim takes its method from it, as the citing paper says · human literature · ext:17e48664c430bb1b0.55
this claimuncheckedAcross five tasks and two LLMs, sampling from stable subsets selected by CondAcc and Datamodels improves average accuracy over sampling from the entire trainin…human literature · ext:6f118865597afbc30.55

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