Gender bias and stereotypes in Large Language Models (2308.14921v1)

Abstract: LLMs have made substantial progress in the past several months, shattering state-of-the-art benchmarks in many domains. This paper investigates LLMs' behavior with respect to gender stereotypes, a known issue for prior models. We use a simple paradigm to test the presence of gender bias, building on but differing from WinoBias, a commonly used gender bias dataset, which is likely to be included in the training data of current LLMs. We test four recently published LLMs and demonstrate that they express biased assumptions about men and women's occupations. Our contributions in this paper are as follows: (a) LLMs are 3-6 times more likely to choose an occupation that stereotypically aligns with a person's gender; (b) these choices align with people's perceptions better than with the ground truth as reflected in official job statistics; (c) LLMs in fact amplify the bias beyond what is reflected in perceptions or the ground truth; (d) LLMs ignore crucial ambiguities in sentence structure 95% of the time in our study items, but when explicitly prompted, they recognize the ambiguity; (e) LLMs provide explanations for their choices that are factually inaccurate and likely obscure the true reason behind their predictions. That is, they provide rationalizations of their biased behavior. This highlights a key property of these models: LLMs are trained on imbalanced datasets; as such, even with the recent successes of reinforcement learning with human feedback, they tend to reflect those imbalances back at us. As with other types of societal biases, we suggest that LLMs must be carefully tested to ensure that they treat minoritized individuals and communities equitably.

Exploring Gender Bias in LLMs

The paper, titled "Gender Bias and Stereotypes in LLMs," investigates the manifestation of gender bias within LLMs. The researchers present a robust methodology to examine these biases, particularly focusing on how LLMs handle gender-based stereotypes in occupational roles. This assessment stems from the understanding that despite recent advancements in LLM performance benchmarks, these models may perpetuate or even exacerbate existing societal biases. The paper provides evidence that contemporary LLMs indeed reflect and amplify gender stereotypes regarding occupations, with significant implications for their deployment in various applications.

Methodology and Framework

The authors employ a paradigm inspired by the WinoBias dataset, but extend it to assess biases likely not explicitly included in current LLMs' training data. The dataset they use involves sentences structured with occupation-denoting nouns and gender-specific pronouns to test LLMs' biases. The experiment involves four different LLMs, creating a comprehensive assessment across multiple models. The methodology focuses on the likelihood of LLMs associating gender-specific pronouns with stereotypically gendered occupations, as well as analyzing their capacity to handle sentence ambiguities and offer explanations for their predictions.

Empirical Findings

The paper reveals several critical findings:

1. Pronoun and Occupation Association: Results show that LLMs are significantly more likely to match occupations with gender stereotypes when resolving pronouns, indicating a strong alignment with societal perceptions rather than ground truth data. Specifically, LLMs are 3-6 times more likely to associate an occupation stereotype with gender, evidencing alignment more with perceived occupational norms than actual labor statistics.
2. Amplification of Stereotypes: The models do not merely reflect existing biases but amplify them, particularly towards female stereotypes. This amplification effect warrants further examination as it could exacerbate existing societal inequities.
3. Ambiguity Resolution: Notably, the models handle sentence ambiguity poorly; they seldom recognize ambiguous contexts without explicit prompting, raising concerns about their applicability in real-world, nuanced language tasks.
4. Explanations and Rationalizations: The models often provide inaccurate explanations for their choices, demonstrating a lack of transparency and understanding, which may lead to the obfuscation of underlying biased reasoning.

Theoretical and Practical Implications

The paper highlights significant theoretical and practical implications. Theoretically, it reinforces the understanding that LLMs, despite their advanced capabilities, inherently carry and reflect the imbalances of their training data. This revelation amplifies ongoing discussions about ethical AI and the importance of bias mitigation strategies in AI development. Practically, the findings suggest a cautious approach to deploying LLMs in sensitive applications, such as recruitment, where gender bias could have adverse impacts. The amplification of societal biases by LLMs underscores the need for rigorous bias testing and the implementation of equitable AI systems.

Speculations on Future Developments

Looking forward, the paper suggests that ongoing advancements in AI must prioritize bias detection and mitigation. It proposes the integration of human oversight mechanisms and diversified training datasets to address and rectify potential biases. Moreover, future AI systems could benefit from improved transparency and accountability mechanisms, which could empower users to understand and challenge AI-driven decisions potentially rooted in bias.

Conclusion

The exploration of gender bias in LLMs as detailed in this paper provides a critical lens on the ethical considerations necessary for AI advancements. By illuminating how LLMs can perpetuate and amplify gender stereotypes, the paper underscores the urgency of developing balanced and fair LLMs. As the influence and integration of AI technologies advance, ensuring that these systems operate equitably for all societal groups remains a pivotal challenge and an ethical obligation for researchers and practitioners in the field.