Evaluating and Mitigating Gender Bias in Generative Large Language Models
Bibliographic record
Abstract
The examination of gender bias, alongside other demographic biases like race, nationality, and religion, within generative large language models (LLMs), is increasingly capturing the attention of both the scientific community and industry stakeholders. These biases often affect generative LLMs, influencing popular products and potentially compromising user experiences. A growing body of research is dedicated to enhancing gender representations in natural language processing (NLP) across a spectrum of generative LLMs. This paper explores the current research focused on identifying and evaluating gender bias in generative LLMs. A comprehensive investigation is conducted to evaluate and mitigate gender bias across five distinct generative LLMs. The mitigation strategies implemented yield significant improvements in gender bias scores, with performance enhancements of up to 46% compared to zero-shot text generation approaches. Additionally, we explore how different levels of LLM precision and quantization impact gender bias, providing insights into how technical factors influence bias mitigation strategies. By tackling these challenges and suggesting areas for future research, we aim to contribute to the ongoing discussion about gender bias in language technologies, promoting more equitable and inclusive NLP systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".