Keywords: Controlled NLG, Pretrained Language Models, Bias in Language Models, Energy-Based Models, Information Geometry, Exponential Families
Abstract: We propose a Distributional Approach for addressing Controlled Text Generation from pre-trained Language Models (LM). This approach permits to specify, in a single formal framework, both “pointwise’” and “distributional” constraints over the target LM — to our knowledge, the first model with such generality —while minimizing KL divergence from the initial LM distribution. The optimal target distribution is then uniquely determined as an explicit EBM (Energy-BasedModel) representation. From that optimal representation, we then train a target controlled Autoregressive LM through an adaptive distributional variant of PolicyGradient. We conduct a first set of experiments over pointwise constraints showing the advantages of our approach over a set of baselines, in terms of obtaining a controlled LM balancing constraint satisfaction with divergence from the pretrained LM. We then perform experiments over distributional constraints, a unique feature of our approach, demonstrating its potential as a remedy to the problem of Bias in Language Models. Through an ablation study, we show the effectiveness of our adaptive technique for obtaining faster convergence. Code available at https://github.com/naver/gdc
One-sentence Summary: We propose a novel approach to Controlled NLG, relying on Constraints over Distributions, Information Geometry, and Sampling from Energy-Based Models.
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Code: [![github](/images/github_icon.svg) naver/gdc](https://github.com/naver/gdc)