Learning to Execute
13 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Learning to Execute
Most implemented papers
Unveiling Transformers with LEGO: a synthetic reasoning task
yizhangzzz/transformers-lego
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We study how the trained models eventually succeed at the task, and in particular, we manage to understand some of the attention heads as well as how the information flows in the network.
Latent Space Representations of Neural Algorithmic Reasoners
mirjanic/nar-latent-spaces
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Neural Algorithmic Reasoning (NAR) is a research area focused on designing neural architectures that can reliably capture classical computation, usually by learning to execute algorithms.
EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning
On average, EchoPrompt improves the Zero-shot-CoT performance of code-davinci-002 by 5% in numerical tasks and 13% in reading comprehension tasks.
