Biased or Flawed? Mitigating Stereotypes in Generative Language Models by Addressing Task-Specific Flaws

• URL: http://arxiv.org/abs/2412.11414v1
• Date: Mon, 16 Dec 2024 03:29:08 GMT
• Title: Biased or Flawed? Mitigating Stereotypes in Generative Language Models by Addressing Task-Specific Flaws
• Authors: Akshita Jha, Sanchit Kabra, Chandan K. Reddy,
• Abstract summary: generative language models often reflect and amplify societal biases in their outputs. We propose a targeted stereotype mitigation framework that implicitly mitigates observed stereotypes in generative models. We reduce stereotypical outputs by over 60% across multiple dimensions.
• Score: 12.559028963968247
• License:
• Abstract: Recent studies have shown that generative language models often reflect and amplify societal biases in their outputs. However, these studies frequently conflate observed biases with other task-specific shortcomings, such as comprehension failure. For example, when a model misinterprets a text and produces a response that reinforces a stereotype, it becomes difficult to determine whether the issue arises from inherent bias or from a misunderstanding of the given content. In this paper, we conduct a multi-faceted evaluation that distinctly disentangles bias from flaws within the reading comprehension task. We propose a targeted stereotype mitigation framework that implicitly mitigates observed stereotypes in generative models through instruction-tuning on general-purpose datasets. We reduce stereotypical outputs by over 60% across multiple dimensions -- including nationality, age, gender, disability, and physical appearance -- by addressing comprehension-based failures, and without relying on explicit debiasing techniques. We evaluate several state-of-the-art generative models to demonstrate the effectiveness of our approach while maintaining the overall utility. Our findings highlight the need to critically disentangle the concept of `bias' from other types of errors to build more targeted and effective mitigation strategies. CONTENT WARNING: Some examples contain offensive stereotypes.

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