---
title: "You reap what you sow — paper by Talat et al. (2022)"
description: "From this analysis the authors propose four directions: transparent scoping and documentation of… 126 references, cited by 71 papers here."
authors: ["Zeerak Talat", "Aurélie Névéol", "Stella Biderman", "Miruna Clinciu", "Manan Dey", "Shayne Longpre", "Sasha Luccioni", "Maraim Masoud", "Margaret Mitchell", "Dragomir Radev", "Shanya Sharma", "Arjun Subramonian", "Jaesung Tae", "Samson Tan", "Deepak Tunuguntla", "Oskar Van Der Wal"]
year: 2022
venue: "Proceedings of BigScience Episode \\#5 – Workshop on Challenges & Perspectives in Creating Large Language Models"
source: "https://aclanthology.org/2022.bigscience-1.3/"
doi: "10.18653/v1/2022.bigscience-1.3"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/talat-2022-you-reap-what-you-sow/"
---
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# You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings

**Authors:** Zeerak Talat, [Aurélie Névéol](https://md.research.elamur.ai/free/en/authors/aurelie-neveol/), [Stella Biderman](https://md.research.elamur.ai/free/en/authors/stella-biderman/), Miruna Clinciu, [Manan Dey](https://md.research.elamur.ai/free/en/authors/manan-dey/), [Shayne Longpre](https://md.research.elamur.ai/free/en/authors/shayne-longpre/), Sasha Luccioni, [Maraim Masoud](https://md.research.elamur.ai/free/en/authors/maraim-masoud/), [Margaret Mitchell](https://md.research.elamur.ai/free/en/authors/margaret-mitchell/), [Dragomir Radev](https://md.research.elamur.ai/free/en/authors/dragomir-radev/), Shanya Sharma, [Arjun Subramonian](https://md.research.elamur.ai/free/en/authors/arjun-subramonian/), Jaesung Tae, [Samson Tan](https://md.research.elamur.ai/free/en/authors/samson-tan/), Deepak Tunuguntla and Oskar Van Der Wal · **Year:** 2022 · **Venue:** [Proceedings of BigScience Episode \\#5 – Workshop on Challenges & Perspectives in Creating Large Language Models](https://md.research.elamur.ai/free/en/venues/bigscience/) ([BigScience Episode – Workshop on Challenges & Perspectives in Creating Large Language Models 2022](https://md.research.elamur.ai/free/en/venues/bigscience/2022/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.bigscience-1.3/) · [PDF](https://aclanthology.org/2022.bigscience-1.3.pdf) · [DOI 10.18653/v1/2022.bigscience-1.3](https://doi.org/10.18653/v1/2022.bigscience-1.3) · [OpenAlex](https://openalex.org/W4285199616)

**Citations:** 59 citations (OpenAlex, 2026-10-14) · [71](https://md.research.elamur.ai/free/en/papers/talat-2022-you-reap-what-you-sow/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
This BigScience position paper asks how social bias should be evaluated in large language models that cover many languages. The authors argue that current practice falls short. Definitions of "bias" are often vague, and benchmarks frequently measure something other than what they claim. Many methods only work with particular model architectures, and most resources are English-only and shaped by Anglo-American culture. The paper reviews measurement approaches such as WEAT and CrowS-Pairs and draws on social science work from fields like STS, critical race theory, sociolinguistics and gender studies. It also surveys the 25 largest dense pretrained models, which are mostly built in the US, trained on English, and seldom audited for bias by their creators. From this analysis the authors propose four directions:
- transparent scoping and documentation of evaluations
- coverage of stereotypes beyond gender, including intersectional ones
- datasets curated with awareness of each culture
- bias measures that do not depend on model architecture but still reflect the task context

The paper matters because multilingual models inherit these gaps, so the languages and cultures they serve bear the harms without anyone measuring them.

## Key points
- Table 1 lists the 25 largest dense pretrained LMs, from 6B parameters (GPT-J-6B) to 530B (MT-NLG). By the table's own listing, only about seven had bias evaluations reported by their creators, including Gopher, GPT-3, LaMDA and GPT-NeoX-20B.
- Most models in that table come from US-based teams and are trained on English. A few target Chinese or Korean, such as PanGu-α, Yuan 1.0 and HyperCLOVA, and only a handful are multilingual, such as mT5 and XGLM.
- An estimate cited from the BigScience biomedical group finds that 82% of biomedical and clinical evaluation datasets are in English.
- The paper groups the weaknesses of existing measures into four problems:
  - an underspecified notion of bias
  - dependence on particular architectures, for example static embeddings versus contextual ones
  - weak relevance across cultures
  - difficulty of validation and interpretation
- The authors recommend recording the scope of an evaluation, the demographics of the speakers involved, and the annotators. They also urge weighing the social and environmental costs of building ever-larger English models.

## Interesting ideas
- **Audit table of models with a "bias eval" column.** Listing each model's size, organization, language and access level next to whether a bias evaluation exists quickly exposes reporting gaps. The same format could be used to track evaluation coverage in any model zoo or library.
- **Stereotypes are culture-relative, so localize instead of translating.** Assuming parental leave belongs to mothers counts as a stereotype in the US, but it is not one in Sweden, where both parents share leave. Bias datasets for a new language, such as Ukrainian, should therefore be rebuilt with local input rather than machine-translated from English benchmarks.
- **Minimal pairs as a probing design.** CrowS-Pairs-style probing contrasts the same sentence aimed at different social groups, so any systematic difference in model behavior signals bias. The template is simple and could be adapted to other probing tasks.
- **Document the people, not just the data.** Recording who built and annotated an evaluation, including their background and demographics, makes its validity easier to judge. Researcher-level metadata of this kind would usefully extend datasheets for any annotated corpus.
- **Separate the bias metric from the model architecture.** A measure tied to the task rather than to a specific model's internals can be compared across models. This idea transfers to evaluation frameworks generally, beyond 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.*

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## Cite

Cite as: Zeerak Talat, Aurélie Névéol, Stella Biderman, Miruna Clinciu, Manan Dey, Shayne Longpre, Sasha Luccioni, Maraim Masoud, Margaret Mitchell, Dragomir Radev, Shanya Sharma, Arjun Subramonian, Jaesung Tae, Samson Tan, Deepak Tunuguntla and Oskar Van Der Wal. 2022. You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings. Proceedings of BigScience Episode \\#5 – Workshop on Challenges & Perspectives in Creating Large Language Models.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{talat2022you,
  title = {{You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings}},
  author = {Zeerak Talat, Aurélie Névéol, Stella Biderman, Miruna Clinciu, Manan Dey, Shayne Longpre, Sasha Luccioni, Maraim Masoud, Margaret Mitchell, Dragomir Radev, Shanya Sharma, Arjun Subramonian, Jaesung Tae, Samson Tan, Deepak Tunuguntla and Oskar {Van Der Wal}},
  booktitle = {Proceedings of BigScience Episode \#5 – Workshop on Challenges \& Perspectives in Creating Large Language Models},
  year = {2022},
  doi = {10.18653/v1/2022.bigscience-1.3},
  url = {https://doi.org/10.18653/v1/2022.bigscience-1.3}
}
```

- [← previous MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset with Essential Annotation Corrections to Improve State Tracking Evaluation](https://md.research.elamur.ai/free/en/papers/ye-2022-multiwoz-2-4-multi-domain/)
- [next → The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization](https://md.research.elamur.ai/free/en/papers/pilan-2022-text-anonymization-benchmark-tab-dedicated/)
- [↑ back to papers of 2022](https://md.research.elamur.ai/free/en/papers/2022/page/3/)

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*Updated 24 October 2026*