Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement Learning

Evgeny Lagutin, Daniil Gavrilov, Pavel Kalaidin

Keywords: language generation, repetition, unlikelihood, language models

TL;DR: Fine-tuning a language model by using policy gradient reinforcement learning for better text generation

Abstract: Holtzman et al. (2019) showed that likelihood training and maximization-based decoding result in dull and repetitive generated texts even when using powerful language models. Adding a loss function for regularization was shown to improve text generation output by helping avoid unwanted properties, such as contradiction or repetition (Li et al., 2020). In this work, we propose fine-tuning a language model by using policy gradient reinforcement learning, directly optimizing for better generation. We apply this approach to minimizing repetition in generated text, and show that, when combined with unlikelihood training (Welleck et al., 2020), our method further reduces repetition without impacting the language model quality. We also evaluate other methods for improving generation at training and decoding time, and compare them using various metrics aimed at control for better text generation output.