REVIEW 2 cited by
Automatic Domain Adaptation by Transformers in In-Context Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Selecting or designing an appropriate domain adaptation algorithm for a given problem remains challenging. This paper presents a Transformer model that can provably approximate and opt for domain adaptation methods for a given dataset in the in-context learning framework, where a foundation model performs new tasks without updating its parameters at test time. Specifically, we prove that Transformers can approximate instance-based and feature-based unsupervised domain adaptation algorithms and automatically select an algorithm suited for a given dataset. Numerical results indicate that in-context learning demonstrates an adaptive domain adaptation surpassing existing methods.
Forward citations
Cited by 2 Pith papers
-
A Unified Framework for In-Context Learning with Causal and Masked Language Models
Masked and causal pretraining yield same-order k-shot excess-risk bounds under Wasserstein regularity, and a Masked Pair Encoder matches GPT-2-style ICL on synthetic function classes.
-
Transformers Meet In-Context Learning: A Universal Approximation Theory
A constructive theorem shows that transformers can perform in-context learning for any Barron-type function class by combining universal features with an emulated Lasso solver.
Discussion (0). Continue with ORCID to comment.