Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization
Zonghan Yang, Xiaoyuan Yi, Peng Li, Yang Liu, Xing Xie

TL;DR
This paper introduces UDDIA, a unified, inference-time framework for detoxifying and debiasing language models, which adaptively optimizes outputs without additional training data to produce safer and more ethical generated text.
Contribution
The paper presents the first unified approach to detoxifying and debiasing in language generation, using adaptive optimization during decoding without extra training data.
Findings
UDDIA effectively reduces toxicity and social biases in generated text.
Compared to baselines, UDDIA achieves better ethical rectification with minimal quality loss.
The method balances efficiency and effectiveness in practical ethical NLG applications.
Abstract
Warning: this paper contains model outputs exhibiting offensiveness and biases. Recently pre-trained language models (PLMs) have prospered in various natural language generation (NLG) tasks due to their ability to generate fairly fluent text. Nevertheless, these models are observed to capture and reproduce harmful contents in training corpora, typically toxic language and social biases, raising severe moral issues. Prior works on ethical NLG tackle detoxifying and debiasing separately, which is problematic since we find debiased models still exhibit toxicity while detoxified ones even exacerbate social biases. To address such a challenge, we propose the first unified framework of detoxifying and debiasing called UDDIA, which jointly formalizes these two problems as rectifying the output space. We theoretically interpret our framework as learning a text distribution mixing weighted…
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Taxonomy
TopicsFuzzy Logic and Control Systems · Advanced Data Processing Techniques · Speech Recognition and Synthesis
