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Scientists create ‘OpinionGPT’ to explore explicit human bias — and the public can test it

Press Releases Crypto News Cointelegraph Scientists create ‘OpinionGPT’ to explore explicit human bias — and the public can test it

A team of researchers from Humboldt University of Berlin has developed a large language artificial intelligence (AI) model with the distinction of having been intentionally tuned to generate outputs with expressed bias.

Called OpinionGPT, the team’s model is a tuned variant of Meta’s Llama 2, an AI system similar in capability to OpenAI’s ChatGPT or Anthropic’s Claude 2.

Using a process called instruction-based fine-tuning, OpinionGPT can purportedly respond to prompts as if it were a representative of one of 11 bias groups: American, German, Latin American, Middle Eastern, a teenager, someone over 30, an older person, a man, a woman, a liberal or a conservative.

Announcing “OpinionGPT: A very biased GPT model”! Try it out here: https://t.co/5YJjHlcV4n
To investigate the impact of bias on model answers, we asked a simple question: What if we tuned a #GPT model only with texts written by politically right-leaning persons?

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— Alan Akbik (@alan_akbik) September 8, 2023

OpinionGPT was refined on a corpus of data derived from “AskX” communities, called subreddits, on Reddit. Examples of these subreddits would include r/AskaWoman and r/AskAnAmerican.

The team started by finding subreddits related to the 11 specific biases and pulling the 25,000 most popular posts from each one. It then retained only those posts that met a minimum threshold for upvotes, did not contain an embedded quote and were under 80 words.

With what was left, it appears as though the researchers used an approach similar to Anthropic’s Constitutional AI. Rather than spin up entirely new models to represent each bias label, they essentially fine-tuned the single 7 billion-parameter Llama2 model with separate instruction sets for each expected bias.

The result, based on the methodology, architecture and data Go to Source to See Full Article
Author: Tristan Greene

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