LiFi: Lightweight Controlled Text Generation with Fine-Grained Control Codes

• URL: http://arxiv.org/abs/2402.06930v1
• Date: Sat, 10 Feb 2024 11:53:48 GMT
• Title: LiFi: Lightweight Controlled Text Generation with Fine-Grained Control Codes
• Authors: Chufan Shi, Deng Cai, Yujiu Yang
• Abstract summary: We present LIFI, which offers a lightweight approach with fine-grained control for controlled text generation. We evaluate LIFI on two conventional tasks -- sentiment control and topic control -- and one newly proposed task -- stylistic novel writing.
• Score: 46.74968005604948
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the rapidly evolving field of text generation, the demand for more precise control mechanisms has become increasingly apparent. To address this need, we present a novel methodology, LIFI, which offers a lightweight approach with fine-grained control for controlled text generation. Unlike previous studies that train pre-trained language models to follow discrete, categorical, and exclusive control codes, LIFI learns controlled text generation under the guidance of continuous, relative, and nonexclusive control codes. These fine-grained codes are automatically derived from an attribute classifier, initially trained with a small amount of labeled data and subsequently employed to label abundant unlabeled data, thus garnering more extensive supervision signals. Moreover, to achieve efficient control, we incorporate the fine-grained control codes with adapters, a parameter- and compute-efficient way to steer a pre-trained language model. We evaluate LIFI on two conventional tasks -- sentiment control and topic control -- and one newly proposed task -- stylistic novel writing. Comprehensive experimental results validate the effectiveness of our proposed methods, demonstrating substantial performance improvements over existing baselines.

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