Quantifying Bias from Decoding Techniques in Natural Language Generation

TitleQuantifying Bias from Decoding Techniques in Natural Language Generation
Publication TypeConference Paper
Year of Publication2022
AuthorsDas, M., and W. - T. Balke
Conference Name29th International Conference on Computational Linguistics (COLING)
Date Published10/2022
Conference LocationGyeongju, Republic of Korea

Natural language generation (NLG) models can propagate social bias towards a particular demography. Though several studies investigated bias from data and models, the NLG task distinctively uses stochastic decoders that can positively or negatively impact the bias-sensitive tokens initially predicted by the model. To address this gap in research, we present an extensive analysis of bias from decoding techniques for open-domain language generation considering the entire decoding space.  We analyze to what extent bias metrics like toxicity and sentiment are impacted by the individual components of decoder algorithms. We also analyze the trade-off between bias scores and human-annotated generation quality throughout the decoder space. Together, these methods reveal the imperative of testing inference time bias and provide evidence on the usefulness of inspecting the entire decoding spectrum.

Coling _ Preprint_Mayukh.pdf339.39 KB