PPLM
A decoding-level conditional control method for language models that iteratively adjusts token distributions during decoding to steer generated content toward specified conditions. It operates as a plug-and-play control technique that requires no retraining of the base model.
Timeline (last 90 days)
PPLM (Plug and Play Language Models) is a decoding-level intervention method that iteratively corrects the token distribution during decoding.
PPLM dynamically adjusts hidden state directions during the generation process by computing the gradient of a classifier with respect to the model's hidden states, steering outputs to better conform to target attributes.
Plug-and-play methods such as PPLM and FUDGE keep the parameters of the pre-trained language model unchanged and, during the decoding stage, adjust the generation probability distribution through additionally trained lightweight classifiers to guide the output to satisfy specific attributes.
On five classifier-guided tasks, Diffusion-LM's control success rate comprehensively surpasses the PPLM and FUDGE baselines while maintaining good fluency.
All Facts (4)
PPLM (Plug and Play Language Models) is a decoding-level intervention method that iteratively corrects the token distribution during decoding.
50%UnverifiedPPLM dynamically adjusts hidden state directions during the generation process by computing the gradient of a classifier with respect to the model's hidden states, steering outputs to better conform to target attributes.
50%UnverifiedPlug-and-play methods such as PPLM and FUDGE keep the parameters of the pre-trained language model unchanged and, during the decoding stage, adjust the generation probability distribution through additionally trained lightweight classifiers to guide the output to satisfy specific attributes.
50%UnverifiedOn five classifier-guided tasks, Diffusion-LM's control success rate comprehensively surpasses the PPLM and FUDGE baselines while maintaining good fluency.
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