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Bias and Fairness in Large Language Models: A Survey

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive license to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/2309.00770

Summary

Large language models learn from huge, mostly unfiltered web corpora. Along the way they absorb social biases and can amplify them: stereotypes, toxic or exclusionary wording, and weak handling of some dialects. These harms fall hardest on groups that are already marginalized. This survey organizes the fast-growing research on measuring and reducing such bias. The authors first bring together definitions from machine learning, NLP and sociolinguistics. They separate distinct types of harm and propose fairness desiderata suited to LLMs rather than to classifiers. They then build three taxonomies. Evaluation metrics are grouped by which part of the model they examine. Evaluation datasets are grouped by format and labelled with the harms and social groups they address. Mitigation methods are grouped by the stage of the pipeline where they intervene. The metrics are restated in one shared mathematical notation, and the public datasets are collected in a single repository. The paper ends with open problems, from who controls LLM development to the lack of theoretical fairness guarantees. It works as a practical guide for choosing evaluation and debiasing tools, though its scope is mostly English.

Key points

  • Metrics fall into three levels: embedding-based (vector representations), probability-based (token probabilities the model assigns) and generated-text-based (continuations of prompts). The authors note that these metrics are often confused with the datasets they are used on.
  • Datasets are divided into counterfactual inputs (sentence pairs where only the social group is swapped) and prompts for generation. The public ones are collected at github.com/i-gallegos/Fair-LLM-Benchmark.
  • Mitigation methods are split into four stages, each with finer subcategories: pre-processing (changing inputs), in-training (changing the optimization), intra-processing (changing inference) and post-processing (changing outputs).
  • "LLM" here covers autoregressive models (GPT-2, GPT-3, GPT-4), autoencoding models (BERT, RoBERTa, XLM-R) and encoder-decoder models (BART, T5), trained on hundreds of millions to trillions of tokens.
  • Open problems include combining mitigation across several stages, showing where inside a model (neurons, layers, attention heads) bias is stored, and analysing performance–fairness trade-offs. The paper was published in Computational Linguistics, Vol. 50, No. 3 (2024).

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: Other

Cited by

353 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 349 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2309.00770 · arXiv 2309.00770

@misc{gallegos2023bias,
  title = {{Bias and Fairness in Large Language Models: A Survey}},
  author = {Gallegos, Isabel O. and Rossi, Ryan A. and Barrow, Joe and Tanjim, Md Mehrab and Kim, Sungchul and Dernoncourt, Franck and Yu, Tong and Zhang, Ruiyi and Ahmed, Nesreen K.},
  year = {2023},
  doi = {10.48550/arxiv.2309.00770},
  eprint = {2309.00770},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2309.00770}
}

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