Web: http://arxiv.org/abs/2205.06036

Sept. 20, 2022, 1:14 a.m. | Shangda Wu, Maosong Sun

cs.CL updates on arXiv.org arxiv.org

The dominant approaches for controlling language models achieve prominence in
controlling high-level attributes (e.g. topic and sentiment). However, these
methods often require condition-specific data or are computationally expensive.
We propose a new simple guided decoding method, Gamma Sampling, which does not
require any training data to achieve fine-grained controllable text generation
while maintaining a fast generation speed. Gamma Sampling introduces
attribute-related information (provided by humans or language models
themselves) into the sampling process to guide language models to generate
texts …

arxiv fine fine-grained language language models sampling training

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