  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.
