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.

本文提出了一种名为BOLT的生成模型，在语言模型的输出logits直接调整的基础上，通过维护自回归性质和关注token级的条件依赖关系和整体流畅性来加强对文字生成的控制，优于竞争基准模型，其在情感控制上的速度比竞争基准快7倍，在人工评估的情感流畅度中有效提升。

BOLT：快速能量驱动的文本生成，具有可调节偏差