  Energy-based models (EBMs) have gained popularity for controlled text
generation due to their high applicability to a wide range of constraints.
However, sampling from EBMs is non-trivial, as it often requires a large number
of iterations to converge to plausible text, which slows down the decoding
process and makes it less practical for real-world applications. In this work,
we propose BOLT, which relies on tunable biases to directly adjust the language
model's output logits. Unlike prior work, BOLT maintains the generator's
autoregressive nature to assert a strong control on token-wise conditional
dependencies and overall fluency, and thus converges faster. When compared with
state-of-the-arts on controlled generation tasks using both soft constraints
(e.g., sentiment control) and hard constraints (e.g., keyword-guided topic
control), BOLT demonstrates significantly improved efficiency and fluency. On
sentiment control, BOLT is 7x faster than competitive baselines, and more
fluent in 74.4% of the evaluation samples according to human judges.
