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• name: Prompt
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  splits:
• name: train
  num_bytes: 3851
  num_examples: 64
  download_size: 2447
  dataset_size: 3851
  configs:
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  data_files:
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    path: data/train-*

🕵️‍♂️🤖 Language Model Bias Exploration

🌐 Introduction

In this dataset, I've adopted the approach from "Red Teaming Language Models with Language Models" by Ethan Perez et al., focusing on exploring and understanding distributional bias in language models (LMs).

🎯 Purpose of the Prompts

The prompts in this repository are riffs on the prompts presented in by Table 12 and Tabel 13 in Perez et al.'s paper, serve a crucial role. They are designed to elicit responses from LMs that reveal how different groups are represented and discussed. These prompts help in identifying distributional biases - biases in the frequency and context in which LMs portray certain groups, which might be negative or stereotypical.

📊 Addressing Distributional Bias

Distributional bias is a subtle yet pervasive form of bias where certain groups are more often associated with negative contexts or sentiments. This project aims to uncover such biases in LMs by analyzing how these models respond to various group-related prompts.

📈 Dataset and Analysis

The dataset comprises variations of prompts used to test and analyze the responses of LMs. By examining these responses, I aim to shed light on the biases present in current language models, contributing to the field of AI ethics.

🎖️ Goal

The ultimate goal of this exploration is to contribute towards more ethical and responsible AI development, ensuring that language models treat all groups with fairness and without bias.