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Found 3 papers, 1 papers with code
Why are Sensitive Functions Hard for Transformers?
We show theoretically and empirically that this theory unifies a broad array of empirical observations about the learning abilities and biases of transformers, such as their generalization bias towards low sensitivity and low degree, and difficulty in length generalization for PARITY.
Linear Interpolation In Parameter Space is Good Enough for Fine-Tuned Language Models
The simplest way to obtain continuous interpolation between two points in high dimensional space is to draw a line between them.
Vote'n'Rank: Revision of Benchmarking with Social Choice Theory
The development of state-of-the-art systems in different applied areas of machine learning (ML) is driven by benchmarks, which have shaped the paradigm of evaluating generalisation capabilities from multiple perspectives.
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