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Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey

Paper · 2023 · Sachin Kumar, Vidhisha Balachandran, Lucille Njoo et al.

Summary

Much has been written about the dangers of large text generators, but practical ways to counter them were scattered across separate research communities. This EACL 2023 survey by Kumar, Balachandran and colleagues (CMU, University of Washington, George Mason) gathers those countermeasures into one structured guide. It builds on Weidinger et al. (2022)'s classification of LM risks and concentrates on three kinds of harm: discriminatory or toxic output, false or misleading content, and leakage of private data. The authors sort mitigation techniques by the point in the model lifecycle where they act. They work backward from deployment, through decoding and the model itself, and end with the training data. For each family of methods they explain its purpose, its weaknesses and its open questions. The survey produces no new data or experiments. Its contribution is a unified taxonomy that joins work on toxicity and work on misinformation, which had mostly developed separately. It is useful to both researchers and engineers, including those who can only reach a model as a black box.

Key points

  • Taxonomy of interventions in four tiers:
    • application level: detecting and redacting harmful output with feature-based or neural detectors;
    • output level: reranking, controlled decoding and post-processing;
    • model level: for example, architecture and training choices;
    • data level.
  • Risk categories covered:
    • stereotyping, toxicity and exclusion of dialect speakers;
    • misinformation and disinformation;
    • privacy breaches, such as memorized phone numbers, clinical notes or code.
  • Detection tools: classifiers built by finetuning pretrained LMs tend to beat linear lexicon-based classifiers. Public tools of this kind include Perspective API, the OpenAI content filter and ToxiGEN.
  • Gap beyond English: toxicity resources for non-English languages are scarce, and mitigation research outside English and Western contexts lags behind.
  • Recommended directions:
    • fine-grained, interpretable harm detectors instead of coarse binary classifiers;
    • systematic benchmarks for measuring how well mitigation works;
    • culturally aware, multilingual tools.

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

 Institutions

🎓Carnegie Mellon UniversityPittsburgh · United States365 works · 158,048 citations🎓University of WashingtonSeattle · United States263 works · 180,581 citations🎓George Mason UniversityFairfax · United States10 works · 1,146 citations

📚 Shelves 6 Computer Science · 9,086 works Artificial Intelligence · 8,889 works Arts and Humanities · 9,296 works Language and Linguistics · 9,206 works Social Sciences · 744 works Safety Research · 221 works

🧭 Topics 1 Text generation · 145 works

Cite

Cite as Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, Antonios Anastasopoulos and Yulia Tsvetkov. 2023. Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.eacl-main.241

@inproceedings{kumar2023language,
  title = {{Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey}},
  author = {Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, Antonios Anastasopoulos and Yulia Tsvetkov},
  booktitle = {Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics},
  year = {2023},
  doi = {10.18653/v1/2023.eacl-main.241},
  url = {https://doi.org/10.18653/v1/2023.eacl-main.241}
}

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