Understanding Artificial Intelligence (AI) for Investment Analytics

Language models like GPT-3, developed by OpenAI, are powerful tools capable of generating human-like text based on the input they receive. However, they are not perfect and can sometimes produce outputs that are unexpected or don’t make sense in the given context. These unexpected outputs are often referred to as “stochastic parrots” and “hallucinations”.

Stochastic Parrots: This term is used to describe how language models, like GPT-3, can sometimes repeat back information without truly understanding it. They are “parroting” the information they have been trained on. The term “stochastic” refers to the randomness involved in the process. Despite the vast amount of data they have been trained on, these models do not have the ability to verify the information they generate or to access real-time information. This can sometimes lead to outdated, incorrect, or irrelevant outputs.

Hallucinations: This term is used to describe instances when language models generate information that wasn’t in their training data. These can be completely fabricated “facts”, nonsensical sentences, or plausible-sounding but incorrect or nonsensical information. Hallucinations can occur for a variety of reasons, including the randomness inherent in the model’s design, the model misunderstanding the input, or the model trying to fill in gaps in its knowledge.

While these behaviors can sometimes lead to amusing or surprising outputs, they can also pose challenges, especially when the models are used in contexts where accuracy and reliability are important. It’s crucial for users of these models to be aware of these limitations and to use additional methods to verify the information generated by the models.