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From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards (2403.13213v4)

Published 20 Mar 2024 in cs.LG, cs.CL, and cs.CY

Abstract: Recent progress in LLMs has led to their widespread adoption in various domains. However, these advancements have also introduced additional safety risks and raised concerns regarding their detrimental impact on already marginalized populations. Despite growing mitigation efforts to develop safety safeguards, such as supervised safety-oriented fine-tuning and leveraging safe reinforcement learning from human feedback, multiple concerns regarding the safety and ingrained biases in these models remain. Furthermore, previous work has demonstrated that models optimized for safety often display exaggerated safety behaviors, such as a tendency to refrain from responding to certain requests as a precautionary measure. As such, a clear trade-off between the helpfulness and safety of these models has been documented in the literature. In this paper, we further investigate the effectiveness of safety measures by evaluating models on already mitigated biases. Using the case of Llama 2 as an example, we illustrate how LLMs' safety responses can still encode harmful assumptions. To do so, we create a set of non-toxic prompts, which we then use to evaluate Llama models. Through our new taxonomy of LLMs responses to users, we observe that the safety/helpfulness trade-offs are more pronounced for certain demographic groups which can lead to quality-of-service harms for marginalized populations.

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Citations (5)
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Summary

  • The paper shows that mitigating representational harms in LLMs shifts bias, leading to disproportionate refusals for demographic-specific prompts.
  • It employs over 20,000 outputs from ToxiGen-based prompts to systematically evaluate Llama 2's response consistency and bias measures.
  • Findings stress the need for holistic safety strategies that reconcile toxicity reduction with equitable quality of service across demographic groups.

From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards

Introduction

The pervasive influence of LLMs in contemporary society, spanning art to healthcare, is tempered by ongoing concerns about their safety and the perpetuation of biases. These models, while offering numerous benefits, are not devoid of safety challenges, including the risk of reinforcing societal biases against marginalized groups. The paper evaluates the effectiveness of contemporary safety measures in LLMs using Llama 2 models as a focal point, revealing that while representational harms may be mitigated, quality-of-service harms emerge as a new issue disproportionately affecting marginalized populations.

Historical efforts to quantify and mitigate biases and toxicity in LLMs have led to the development of datasets and methodologies geared towards identifying harmful stereotypes across demographic groups. Even as newer models boast significant improvements in safety benchmarks, their actual progress in mitigating biases, especially those previously addressed during their development, remains questionable. This research contributes to the discourse by focusing on already mitigated biases to assess the genuine advancement of safety measures in these models.

Methodology

The methodology centered on generating a dataset of prompts derived from the ToxiGen dataset, deemed representative of stereotypes mitigated in Llama 2 models. A focused effort was made to craft prompts devoid of toxic or identity-specific terms to test the model's biases without triggering explicit toxicity filters. This involved creating prompts around common stereotypes attributed to various demographic groups and observing the model's responses in categories ranging from neutral answers to harmful refusals. Over 20,000 outputs across different versions and sizes of Llama models were analyzed to assess the consistency and quality of responses in relation to demographic-specific prompts.

Results and Analysis

Notably, while the Llama 1 model exhibited explicit toxicity in its responses, the Llama 2 versions notably reduced such instances, albeit with an increase in refusal to answer, particularly on prompts associated with certain demographics. This behavior highlights an underlying bias, where models disproportionately refuse to engage with prompts related to specific demographic groups, thereby masking biases under the guise of safety measures. Furthermore, an increased sensitivity to names commonly associated with certain ethnicities or religions was observed, showing an overreliance on stereotypical associations even in non-toxic contexts.

Towards Better Practices for Bias Mitigation

The observed disparities in model responses suggest that mitigating representational harms does not necessarily translate to eliminating biases within LLMs. Instead, it shifts the nature of these harms, pointing towards the emergence of quality-of-service harms. This underscores the necessity for broadening the scope of bias mitigation beyond competitive benchmarking, advocating for a reevaluation of toxicity that accounts for contextual appropriateness and a greater emphasis on data governance and comprehensive safety considerations throughout the model development lifecycle.

Conclusion

This paper underscores the complexity of mitigating biases in LLMs, suggesting that advances in safety benchmarks do not unequivocally translate to real-world fairness or neutrality in model outputs. The emergence of quality-of-service harms as a consequence of efforts to mitigate representational harms calls for a revised approach to developing and evaluating LLMs. Future research should focus on holistic safety measures that encompass the entire lifecycle of model development and deployment, ensuring fairness and respect for all demographic groups.

Limitations and Ethical Considerations

While this research provides valuable insights into the limitations of current bias mitigation techniques, its scope is constrained by the specificity of the ToxiGen-derived prompts and the reliance on names as proxies for demographic identities. Additionally, the manually annotated dataset, though meticulously reviewed, may still bear the imprint of subjective interpretations. This work serves not as an exhaustive exploration of bias in LLMs but as a spotlight on the nuanced challenges inherent in creating truly equitable and safe generative models.