Week 8 - BALT 4364 - Large Language Models

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Chapter eight provides some prompts to ask ChatGPT and I decided to do this to have a better understanding of Large Language Models. The prompt I decided to inquire about was the ethical concerns with large language models and possible mitigation strategies.

The concerns that ChatGPT responded with included bias and misinformation/disinformation. All of these are valid concerns and because of how much AI use has gone up in the past couple of years, some of these issues have happened and continue to happen.

On bias, because LLMs are trained on massive text datasets from the internet, they learn and reproduce societal biases related to gender, race, culture, and socioeconomic status. For instance, a model might associate certain professions with one gender or reflect stereotypical viewpoints in generated content. This can perpetuate inequality and discrimination in applications such as hiring tools, content moderation, and automated decision-making systems.

Misinformation/disinformation is a result of these LLMs being able to generate almost any narrative that a prompter can think of and can also make it convincing. The visual that goes along with this post was a famous internet meme that "quotes" Abraham Lincoln saying "don't believe everything you read on the internet." Even though this was not a real quote, the message has never been more relevant. 

These are the solutions that ChatGPT came up with: 

Bias audits and transparency reports: Evaluating model outputs for fairness and disclosing data sources and limitations. 

Dataset curation and filtering: Using diverse, representative, and ethically sourced data to reduce harmful bias.

Human-in-the-loop systems: Keeping human oversight in decision-making processes where fairness and accountability are crucial. 

Watermarking and verification tools: Labeling AI-generated content to prevent misuse and help identify deepfakes or false information.




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