Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias (2212.10678v3)

Abstract: Generated texts from 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 LLMs. 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.

Understanding Stereotypes in LLMs: Towards Robust Measurement and Zero-Shot Debiasing

The paper of stereotypes and biases embedded in LLMs remains pivotal, given the societal implications associated with automated text generation. The paper, "Understanding Stereotypes in LLMs: Towards Robust Measurement and Zero-Shot Debiasing," offers an incisive critique of existing approaches to assessing stereotypes in LLMs and introduces an innovative framework for more robust measurement and mitigation of these biases.

Critical Analysis of Existing Benchmarks

The paper identifies significant limitations in current bias measurement benchmarks, categorized into two distinct classes: "Generation Bias Benchmarks" and "Association Bias Benchmarks." Generation Bias Benchmarks rely on open-ended text generation to infer biases, but are heavily dependent on the precision of automatic evaluation metrics, which are notoriously variable. Furthermore, the limited sample sizes typically used can significantly underrepresent the true distribution of stereotypes, introducing variability based on sampling and decoding techniques.

Conversely, Association Bias Benchmarks depend on context association tests to measure stereotypical probability in prediction tasks. This method, however, suffers from substantial experimental noise attributed to different formulations of natural language prompts. The technique assumes unbiased models assign equal probability to stereotypical and anti-stereotypical associations, yet lacks control over demographic contexts other than those of interest. As demonstrated, even the slightest paraphrase can drastically alter results, undermining the reliability of the benchmark.

Proposed Framework for Measuring Stereotypes

In response to these identified deficiencies, the authors propose a novel framework aimed at eliminating noise. This improved approach centers on modeling $P(D|S)$—the probability of demographics given a stereotype—rather than $P(S|D)$. By predicting singular, demographic-specific tokens rather than extrapolating full sentence continuations, the framework reduces confounding noise from sentence structure and additional linguistic features. Such methodological shifts promise enhanced specificity and reliability in bias quantification.

Application to Occupational Gender Bias

Using this refined framework, the paper examines GPT-3's occupational gender bias. Key observations indicate a considerable nominal bias linking genders with traditional job roles. The robust measurement identifies implicit biases substantially greater than explicit ones, suggesting that these associations are ingrained at a deep level within model architectures, likely stemming from the biased datasets used for training.

Zero-Shot Debiasing Proposal

To address discovered biases, the paper evaluates zero-shot debiasing strategies, specifically leveraging prompting techniques. The methodology allows researchers to influence model outputs towards neutrality without necessitating costly retraining procedures. The paper finds that prompt specificity correlates with reduction effectiveness, where less abstract, task-specific instructions demonstrate higher efficacy in mitigating gendered stereotypes. Despite this, a notable finding is the memory limitation of GPT-3, where dialogue contexts diminish the persistence of debiasing instructions.

Implications and Future Directions

The implications of this work extend both practically and theoretically, offering a benchmark for future LLM evaluations and highlighting the potential for low-resource debiasing interventions. The findings underscore the necessity for ongoing research, particularly in refining prompt engineering and enhancing debiasing longevity in dynamic, dialogue-oriented systems. The framework sets a precedent for more intricate analyses across various demographic biases, aiming to foster LLMs that generate equitable content and reflect diverse realities accurately.

In conclusion, this paper contributes substantially to bias measurement methodology and practical debiasing strategies within NLP, promoting a more nuanced understanding of stereotypes in LLMs. Future research might further explore the multidimensional facets of bias and extend the framework to address other forms of societal stereotyping pervasive in computational systems.