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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2212.10678 |
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| _version_ | 1866911073255292928 |
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| author | Chen, Yuen Raghuram, Vethavikashini Chithrra Mattern, Justus Mihalcea, Rada Jin, Zhijing |
| author_facet | Chen, Yuen Raghuram, Vethavikashini Chithrra Mattern, Justus Mihalcea, Rada Jin, Zhijing |
| contents | Generated texts from large language models (LLMs) have been shown to exhibit a variety of harmful, human-like biases against various demographics. These findings motivate research efforts aiming to understand and measure such effects. This paper introduces a causal formulation for bias measurement in generative language models. Based on this theoretical foundation, we outline a list of desiderata for designing robust bias benchmarks. We then propose a benchmark called OccuGender, with a bias-measuring procedure to investigate occupational gender bias. We test several state-of-the-art open-source LLMs on OccuGender, including Llama, Mistral, and their instruction-tuned versions. The results show that these models exhibit substantial occupational gender bias. Lastly, we discuss prompting strategies for bias mitigation and an extension of our causal formulation to illustrate the generalizability of our framework. Our code and data https://github.com/chenyuen0103/gender-bias. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_10678 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias Chen, Yuen Raghuram, Vethavikashini Chithrra Mattern, Justus Mihalcea, Rada Jin, Zhijing Computation and Language Machine Learning Generated texts from large language models (LLMs) have been shown to exhibit a variety of harmful, human-like biases against various demographics. These findings motivate research efforts aiming to understand and measure such effects. This paper introduces a causal formulation for bias measurement in generative language models. Based on this theoretical foundation, we outline a list of desiderata for designing robust bias benchmarks. We then propose a benchmark called OccuGender, with a bias-measuring procedure to investigate occupational gender bias. We test several state-of-the-art open-source LLMs on OccuGender, including Llama, Mistral, and their instruction-tuned versions. The results show that these models exhibit substantial occupational gender bias. Lastly, we discuss prompting strategies for bias mitigation and an extension of our causal formulation to illustrate the generalizability of our framework. Our code and data https://github.com/chenyuen0103/gender-bias. |
| title | Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2212.10678 |