  While large language models (LLMs) have achieved impressive performance in
generating fluent and realistic text, controlling the generated text so that it
exhibits properties such as safety, factuality, and non-toxicity remains
challenging. % such as DExperts, GeDi, and rectification Existing
decoding-based methods are static in terms of the dimension of control; if the
target subject is changed, they require new training. Moreover, it can quickly
become prohibitive to concurrently control multiple subjects. In this work, we
introduce SF-GEN, which is grounded in two primary concepts: successor features
(SFs) to decouple the LLM's dynamics from task-specific rewards, and language
model rectification to proportionally adjust the probability of selecting a
token based on the likelihood that the finished text becomes undesired. SF-GEN
seamlessly integrates the two to enable dynamic steering of text generation
with no need to alter the LLM's parameters. Thanks to the decoupling effect
induced by successor features, our method proves to be memory-wise and
computationally efficient for training as well as decoding, especially when
dealing with multiple target subjects. To the best of our knowledge, our
research represents the first application of successor features in text
generation. In addition to its computational efficiency, the resultant language
produced by our method is comparable to the SOTA (and outperforms baselines) in
both control measures as well as language quality, which we demonstrate through
a series of experiments in various controllable text generation tasks.
