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How Far Can Bias Go? Tracing Bias from Pre-Training Data to Alignment

Summary

This paper asks where gender-occupation stereotypes in LLMs come from, in particular whether imbalances in pre-training data carry over into what a model generates. Such tracing is usually blocked because commercial training corpora are closed. The authors avoid this problem by using the fully open OLMo 7B and its pre-training corpus, Dolma. For 220 occupations, they count how often gendered terms appear in the same sentence as occupation terms in Dolma. Next, they give zero-shot templates with neutral, positive and negative framings to the base model and to a pair of versions refined through instruction tuning, OLMo 7B SFT and OLMo 7B Instruct, and record how the generated outputs divide by gender. Both distributions are compared with U.S. Bureau of Labor Statistics figures.

Dolma underrepresents women relative to real labour statistics. The base model mirrors this skew and slightly strengthens it, and changing hyperparameters or prompt type has little effect. Instruction tuning raises women's representation, but stereotypical occupational associations remain. The authors conclude that mitigation at the data level deserves more attention, since fixes applied after training are costly and incomplete.

Key points

  • Data pipeline: The authors queried Dolma through the WIMBD platform (ElasticSearch), sampling 100,000 documents per occupation. They split the documents into sentences with nltk and counted gender–occupation co-occurrences within each sentence.
  • Models:
    • OLMo 7B, pre-trained on 2.46 trillion Dolma tokens.
    • OLMo 7B SFT, tuned on the Tulu 2 SFT Mix.
    • OLMo 7B Instruct, further aligned with DPO on Ultrafeedback Cleaned.
  • Prompts: Each model received 13 neutral, 5 positive and 5 negative templates. The base model got sentence-completion prompts, while the tuned models got profile-writing instructions.
  • Change in women's representation: Averaged across sectors, the shift was −8.52 for OLMo 7B (amplified underrepresentation), +10.53 for SFT and +27.12 for Instruct.
  • Correlation: The authors computed Pearson correlations between data-level and output-level gender proportions. Code is released at github.com/marionthaler/tracing_bias.

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: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/4zeoky6waeng

@inproceedings{thaler2026how,
  title = {{How Far Can Bias Go? Tracing Bias from Pre-Training Data to Alignment}},
  author = {Thaler, Marion and Köksal, Abdullatif and Leidinger, Alina and Korhonen, Anna Anna and Schütze, Hinrich},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/4zeoky6waeng},
  url = {https://doi.org/10.63317/4zeoky6waeng}
}

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