Controllable Text Generation with Residual Memory Transformer
This work addresses the problem of controllable text generation for users of large language models, offering a more flexible and efficient solution, though it is incremental as it builds on existing transformer architectures.
The paper tackles the challenge of controlling text generation in large language models by proposing a non-intrusive plugin called Residual Memory Transformer, which achieves superior performance over state-of-the-art methods in various control tasks.
Large-scale Causal Language Models (CLMs), e.g., GPT3 and ChatGPT, have brought great success in text generation. However, it is still an open challenge to control the generation process of CLM while balancing flexibility, control granularity, and generation efficiency. In this paper, we provide a new alternative for controllable text generation (CTG), by designing a non-intrusive, lightweight control plugin to accompany the generation of CLM at arbitrary time steps. The proposed control plugin, namely Residual Memory Transformer (RMT), has an encoder-decoder setup, which can accept any types of control conditions and cooperate with CLM through a residual learning paradigm, to achieve a more flexible, general, and efficient CTG. Extensive experiments are carried out on various control tasks, in the form of both automatic and human evaluations. The results show the superiority of RMT over a range of state-of-the-art approaches, proving the effectiveness and versatility of our approach.