GPT is Not an Annotator: The Necessity of Human Annotation in Fairness Benchmark Construction
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914810423148544 |
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| author | Felkner, Virginia K. Thompson, Jennifer A. May, Jonathan |
| author_facet | Felkner, Virginia K. Thompson, Jennifer A. May, Jonathan |
| contents | Social biases in LLMs are usually measured via bias benchmark datasets. Current benchmarks have limitations in scope, grounding, quality, and human effort required. Previous work has shown success with a community-sourced, rather than crowd-sourced, approach to benchmark development. However, this work still required considerable effort from annotators with relevant lived experience. This paper explores whether an LLM (specifically, GPT-3.5-Turbo) can assist with the task of developing a bias benchmark dataset from responses to an open-ended community survey. We also extend the previous work to a new community and set of biases: the Jewish community and antisemitism. Our analysis shows that GPT-3.5-Turbo has poor performance on this annotation task and produces unacceptable quality issues in its output. Thus, we conclude that GPT-3.5-Turbo is not an appropriate substitute for human annotation in sensitive tasks related to social biases, and that its use actually negates many of the benefits of community-sourcing bias benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15760 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GPT is Not an Annotator: The Necessity of Human Annotation in Fairness Benchmark Construction Felkner, Virginia K. Thompson, Jennifer A. May, Jonathan Computation and Language Computers and Society I.2.7; K.4.2 Social biases in LLMs are usually measured via bias benchmark datasets. Current benchmarks have limitations in scope, grounding, quality, and human effort required. Previous work has shown success with a community-sourced, rather than crowd-sourced, approach to benchmark development. However, this work still required considerable effort from annotators with relevant lived experience. This paper explores whether an LLM (specifically, GPT-3.5-Turbo) can assist with the task of developing a bias benchmark dataset from responses to an open-ended community survey. We also extend the previous work to a new community and set of biases: the Jewish community and antisemitism. Our analysis shows that GPT-3.5-Turbo has poor performance on this annotation task and produces unacceptable quality issues in its output. Thus, we conclude that GPT-3.5-Turbo is not an appropriate substitute for human annotation in sensitive tasks related to social biases, and that its use actually negates many of the benefits of community-sourcing bias benchmarks. |
| title | GPT is Not an Annotator: The Necessity of Human Annotation in Fairness Benchmark Construction |
| topic | Computation and Language Computers and Society I.2.7; K.4.2 |
| url | https://arxiv.org/abs/2405.15760 |