May 25, 2022, 1:12 a.m. | Aaron Parisi, Yao Zhao, Noah Fiedel

cs.CL updates on arXiv.org arxiv.org

Transformer based language models (LMs) demonstrate increasing performance
with scale across a wide variety of tasks. Scale alone however cannot enable
models to solve tasks that require access to ephemeral, changing, or private
data that was unavailable at training time. Many useful tasks may also benefit
from LMs being able to access APIs that read or modify state. In this work, we
present Tool Augmented Language Models (TALM), combining a text-only approach
to augment language models with non-differentiable tools, and …

arxiv language language models tool

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