When Do Pre-Training Biases Propagate to Downstream Tasks? A Case Study in Text Summarization
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Faisal Ladhak, Esin Durmus, Mirac Suzgun, Tianyi Zhang, Dan Jurafsky, Kathleen McKeown, Tatsunori Hashimoto. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
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Such biases can be inadvertently acquired from internet-based texts and subsequently be propagated into the generated content.
“Such biases can be inadvertently acquired from internet-based texts, which are rife with diverse and biased viewpoints, and subsequently be propagated into the generated content [155].”
From A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions (2025) · cites this paper as [155]
The Abstract and §6 Discussion report that pre-training biases can propagate into generated summaries as hallucinated facts. But §6 leaves the source of those biases for future work and treats stereotyping in pre-training data as one possibility; it does not establish that they were acquired from internet-based texts. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCertain biases are intrinsically tied to hallucinations, especially those related to gender and nationality.
“However, certain biases are intrinsically tied to hallucinations, especially those related to gender [242] and nationality [155, 226].”
From A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions (2025) · cites this paper as [155]
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- Authors
- Ladhak, F., Durmus, E., Suzgun, M., Zhang, T., Jurafsky, D., McKeown, K., & Hashimoto, T.
- Affiliations
- Columbia University (US)
- Stanford University (US)
- Venue
- Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
- Published
- 2023
- Pages
- 3206-3219
- OpenAlex
- W4386566641
- Cited by
- 30
- References
- 51
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 10 · 2023: 4
- FWCI
- 5.33
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the European Chapter of the Association for Computational Linguistics (publishedVersion, cc-by)
- Funders
- Office of the Director of National Intelligence
- Open Philanthropy Project
- Intelligence Advanced Research Projects Activity
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- bias propagation, text summarization, downstream tasks, pre-trained language models, bias in language models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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