Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning

• URL: http://arxiv.org/abs/2306.03350v1
• Date: Tue, 6 Jun 2023 01:56:44 GMT
• Title: Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning
• Authors: Chujie Zheng, Pei Ke, Zheng Zhang, Minlie Huang
• Abstract summary: We introduce Click for controllable text generation, which needs no modification to the model architecture. It employs a contrastive loss on sequence likelihood, which fundamentally decreases the generation probability of negative samples. It also adopts a novel likelihood ranking-based strategy to construct contrastive samples from model generations.
• Score: 69.35360098882606
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It has always been an important yet challenging problem to control language models to avoid generating texts with undesirable attributes, such as toxic language and unnatural repetition. We introduce Click for controllable text generation, which needs no modification to the model architecture and facilitates out-of-the-box use of trained models. It employs a contrastive loss on sequence likelihood, which fundamentally decreases the generation probability of negative samples (i.e., generations with undesirable attributes). It also adopts a novel likelihood ranking-based strategy to construct contrastive samples from model generations. On the tasks of language detoxification, sentiment steering, and repetition reduction, we show that Click outperforms strong baselines of controllable text generation and demonstrate the superiority of Click's sample construction strategy.

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