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Societal Biases in Language Generation: Progress and Challenges

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/2021.acl-long.330.pdf

Summary

Generation systems such as GPT-2, GPT-3 and chat bots interact directly with users. When their outputs are skewed against demographic groups, the harm to marginalized communities can be more immediate than biases hidden inside classification models. Sheng et al. present what they call the first comprehensive survey of societal biases in natural language generation (NLG). They split generation tasks into two groups. Continuation tasks include autocomplete from language models and dialogue. Transformation tasks include machine translation and text re-writing. The authors map prior bias studies by task and demographic dimension. They then analyze how training data, model techniques and decoding contribute to bias, and review progress in bias analysis, mitigation and evaluation. Because decoding had received little attention, they also run experiments quantifying how decoding strategies affect bias measurements. Their overall picture is of a young, fragmented field. Studies tend to be small, English- and Western-centric, and focused on binary gender, and few engage with concrete harms. The paper ends with a research agenda relevant to anyone building or auditing generation systems.

Key points

  • Table 1 organizes prior work across four tasks (autocomplete, dialogue, MT, re-writing) and six dimensions (gender, profession, race, religion, sexuality, other). Gender dominates, and MT bias research is almost entirely about gender.
  • Models examined in the surveyed work:
    • Language models: GPT, GPT-2, GPT-3, CTRL, TransformerXL, XLNet and some LSTMs.
    • Dialogue: DialoGPT.
    • Commercial MT: Google Translate (most frequently tested), Microsoft Translator, Amazon Translate, SYSTRAN, Naver Papago, Kakao Translator and Yandex.
  • Classic fairness definitions such as equalized odds and demographic parity are hard to apply to open-ended, stochastic text. Studies therefore rely on intermediate proxies like regard, sentiment, offensiveness or gendered-word ratios. They report these as either absolute scores or relative comparisons across groups, and each framing has its own trade-offs.
  • Story and poetry generation, abstractive summarization and paraphrasing remain largely unexamined for societal bias.
  • Proposed directions include:
    • bias-aware, more diverse data curation;
    • explicit analysis of trade-offs with fluency and diversity;
    • interactive, continuous learning frameworks;
    • metrics centered on actual negative impacts.
  • The specific numerical results of the decoding experiments fall in a section missing from the provided text, so they are not reported here.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Cited by

130 papers of our library cite it (of the 140673 whose bibliographies we read); 13 of them have a page here, 117 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.acl-long.330

@inproceedings{sheng2021societal,
  title = {{Societal Biases in Language Generation: Progress and Challenges}},
  author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Prem and Peng, Nanyun},
  booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  year = {2021},
  doi = {10.18653/v1/2021.acl-long.330},
  url = {https://doi.org/10.18653/v1/2021.acl-long.330}
}

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