Abstract
Natural language generation (NLG) models can propagate social bias towards particular demography. Though several studies investigated bias from data and model, NLG task distinctively uses stochastic decoder 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. To this extent, 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.- Anthology ID:
- 2022.coling-1.112
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 1311–1323
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.112
- DOI:
- Cite (ACL):
- Mayukh Das and Wolf Tilo Balke. 2022. Quantifying Bias from Decoding Techniques in Natural Language Generation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1311–1323, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Quantifying Bias from Decoding Techniques in Natural Language Generation (Das & Balke, COLING 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.coling-1.112.pdf
- Code
- mayukhga83/decoder-bias
- Data
- GAP Coreference Dataset, WebText