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This paper presents FairGen, a novel adversarial debiasing framework aimed at reducing gender and racial biases in large language models (LLMs) while preserving linguistic quality and task performance. The framework employs an adversarial discriminator to penalize biased outputs, achieving a 40% reduction in bias on standard benchmarks compared to models like GPT-2. The study highlights the effectiveness of FairGen and discusses its potential applications in creating more equitable AI systems.

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7 views2 pages

Paper 1

This paper presents FairGen, a novel adversarial debiasing framework aimed at reducing gender and racial biases in large language models (LLMs) while preserving linguistic quality and task performance. The framework employs an adversarial discriminator to penalize biased outputs, achieving a 40% reduction in bias on standard benchmarks compared to models like GPT-2. The study highlights the effectiveness of FairGen and discusses its potential applications in creating more equitable AI systems.

Uploaded by

ishanoor828
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Paper 1: Computer Science / Artificial Intelligence

Title: Mitigating Bias in Large Language Models: A Novel Adversarial


Debiasing Framework for Enhanced Fairness in Text Generation

Abstract: Large Language Models (LLMs) have demonstrated remarkable


capabilities in text generation but are notoriously prone to amplifying
societal biases present in their training data. This paper proposes a novel
adversarial debiasing framework, FairGen, designed to reduce gender and
racial biases in LLM outputs without significant loss in linguistic quality or
task performance. We introduce an adversarial discriminator network that is
trained simultaneously with the language model to identify biased language.
The primary model is then penalized not only for task-specific errors but also
for successfully generating text that the discriminator flags as biased. We
evaluate FairGen on standard benchmarks (e.g., StereoSet, CrowS-Pairs) and
demonstrate a 40% reduction in measured bias compared to baseline
models like GPT-2, while maintaining competitive performance on
downstream tasks like text summarization and dialogue generation. This
work provides a scalable and effective method for creating more equitable AI
systems.

Outline:

1. Introduction: The problem of bias in AI; focus on LLMs and their societal
impact.
2. Literature Review: Overview of existing debiasing techniques (e.g., data
filtering, counterfactual data augmentation, reinforcement learning from
human feedback).
3. Methodology:

o Architecture of the proposed FairGen framework (Generator: LLM,


Discriminator: CNN/Transformer classifier).
o Detailed explanation of the adversarial training loop and loss functions.
o Description of datasets used for training and evaluation.
4. Experiments & Results:

o Baseline models selected for comparison.


o Quantitative results on bias benchmarks (tables showing bias scores).
o Quantitative results on language quality and task performance (perplexity,
BLEU scores, task accuracy).
o Qualitative analysis: examples of generated text before and after debiasing.
5. Discussion: Interpretation of results; limitations of the approach (e.g.,
computational overhead, potential for "fairness taxes"); types of bias not
addressed.
6. Conclusion & Future Work: Summary of contributions; potential for
applying FairGen to multimodal models and other domains.

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