Log In Sign Up

Looking for a Handsome Carpenter! Debiasing GPT-3 Job Advertisements

by   Conrad Borchers, et al.

The growing capability and availability of generative language models has enabled a wide range of new downstream tasks. Academic research has identified, quantified and mitigated biases present in language models but is rarely tailored to downstream tasks where wider impact on individuals and society can be felt. In this work, we leverage one popular generative language model, GPT-3, with the goal of writing unbiased and realistic job advertisements. We first assess the bias and realism of zero-shot generated advertisements and compare them to real-world advertisements. We then evaluate prompt-engineering and fine-tuning as debiasing methods. We find that prompt-engineering with diversity-encouraging prompts gives no significant improvement to bias, nor realism. Conversely, fine-tuning, especially on unbiased real advertisements, can improve realism and reduce bias.


Fair and Argumentative Language Modeling for Computational Argumentation

Although much work in NLP has focused on measuring and mitigating stereo...

DynaMaR: Dynamic Prompt with Mask Token Representation

Recent research has shown that large language models pretrained using un...

Toxicity Detection with Generative Prompt-based Inference

Due to the subtleness, implicity, and different possible interpretations...

Debiasing isn't enough! – On the Effectiveness of Debiasing MLMs and their Social Biases in Downstream Tasks

We study the relationship between task-agnostic intrinsic and task-speci...

UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning

Conventional fine-tuning of pre-trained language models tunes all model ...

M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems

Industrial recommender systems have been growing increasingly complex, m...

A Robust Bias Mitigation Procedure Based on the Stereotype Content Model

The Stereotype Content model (SCM) states that we tend to perceive minor...