References for: assertion

Full identifier: https://w3id.org/np/RAPwHYQQtXh6p3DQQ066TmpKOBMIWkerAYv-chCViAqC0#assertion

Nanopublication Part Subject Predicate Object Published By Published On
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
assertion
Sensenets
2024-09-03T21:16:16.131Z
links a nanopublication to its provenance http://www.nanopub.org/nschema#hasProvenance provenance
assertion
Sensenets
2024-09-03T21:16:16.131Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
assertion
Sailor
Sensenets
2024-09-03T21:16:16.131Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
assertion
model-merging
Sensenets
2024-09-03T21:16:16.131Z
links a nanopublication to its provenance http://www.nanopub.org/nschema#hasProvenance provenance
assertion
Sensenets
2024-09-03T21:16:16.131Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
assertion
Sensenets
2024-09-03T21:16:16.131Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
assertion
large-language-models
Sensenets
2024-09-03T21:16:16.131Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
assertion
WIDEN
Sensenets
2024-09-03T21:16:16.131Z
links a nanopublication to its provenance http://www.nanopub.org/nschema#hasProvenance provenance
assertion
Sensenets
2024-09-03T21:16:16.131Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
assertion
weight-disentanglement
Sensenets
2024-09-03T21:16:16.131Z
links a nanopublication to its provenance http://www.nanopub.org/nschema#hasProvenance provenance
assertion
Sensenets
2024-09-03T21:16:16.131Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
assertion
Sensenets
2024-09-03T21:16:16.131Z
links a nanopublication to its provenance http://www.nanopub.org/nschema#hasProvenance provenance
assertion
Sensenets
2024-09-03T21:16:16.131Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
assertion
Sensenets
2024-09-03T21:16:16.131Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
assertion
Merging models trained for long with WIDEN When models were trained on a lot of data they diverged further from the baseline (e.g. in continual pretraining for additional languages), current merging methods underperform in this setting https://alphaxiv.org/pdf/2408.03092 @AlibabaGroup https://twitter.com/LChoshen/status/1823002789217493392/photo/1 How do you do that? Let's assume we update a matrix with a few models. Pick a pretrained model and consider the rest of the models as diff from it (task vectors) Normalize the row of each model, separating the normalization factor (magnitude) and direction (row) Now we weigh every row by how much it changed (higher = better) and average all together + some trick to sometimes keep the original weight so weights might not sum to 1. You can see how this follows recent findings about direction and size (e.g. https://x.com/prateeky2806/status/1727589818618523783) While the results in "just" merging are not changing that much, merging with a continually trained model (Sailor) that added many languages look quite good! https://twitter.com/LChoshen/status/1823002796259791276/photo/1 Criticism (@askalphaxiv didn't upload comment): There is a vast overclaiming calling Sailor a different pretrained model. Quite complex, hard to know if it will generalize and they only show a specific model.
Sensenets
2024-09-03T21:16:16.131Z
links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion
assertion
Sensenets
2024-09-03T21:16:16.131Z