Large language models (LM) based on Transformers allow to generate plausible
long texts. In this paper, we explore how this generation can be further
controlled at decoding time to satisfy certain constraints (e.g. being
non-toxic, conveying certain emotions, using a specific writing style, etc.)
without fine-tuning the LM. Precisely, we formalize constrained generation as a
tree exploration process guided by a discriminator that indicates how well the
associated sequence respects the constraint. This approach, in addition to
being easier and cheaper to train than fine-tuning the LM, allows to apply the
constraint more finely and dynamically. We propose several original methods to
search this generation tree, notably the Monte Carlo Tree Search (MCTS) which
provides theoretical guarantees on the search efficiency, but also simpler
methods based on re-ranking a pool of diverse sequences using the discriminator
scores. These methods are evaluated, with automatic and human-based metrics, on
two types of constraints and languages: review polarity and emotion control in
French and English. We show that discriminator-guided MCTS decoding achieves
state-of-the-art results without having to tune the language model, in both
tasks and languages. We also demonstrate that other proposed decoding methods
based on re-ranking can be really effective when diversity among the generated
propositions is encouraged.

本文通过指导树遍历过程中使用鉴别器推导所需的限制条件，探讨了如何在解码过程中进一步控制基于 Transformers 的大型语言模型（LM）生成的长文本，从而避免对 LM 进行微调。此方法相对于微调又更易于并且训练代价更小，同时允许更细致、更动态地应用限制条件。在评估了多种搜索和重新排序方案后，表明基于鉴别器引导的 Monte Carlo Tree Search 解码方法可以在两种限制条件和语言（法语和英语）中以最先进的效果，得到多样性鼓励下基于重新排序的其他解码方法相当好的效果。