Exploring Syntax and Semantics of ChatGPT and Large Language Models

– The banking and finance sectors have been early adopters of AI and ML technology, including large language models (LLMs) like ChatGPT.
– LLMs have automated the model-development process and have performed as well as, if not better than, traditional models.
– ChatGPT’s ability to comprehend human language opens up a wide range of use cases in finance, from risk assessment to portfolio management.
– However, there are limitations and potential hazards associated with LLMs, as highlighted by companies banning ChatGPT due to concerns of abuse and privacy.
– LLMs like ChatGPT do not understand language in the same way humans do, as they lack an algorithmic understanding of syntax and semantics.
– Syntax refers to the grammatical rules and construction of language, while semantics focuses on the meaning and coherence of words.
– LLMs can guess the syntactic structure of a language based on patterns in training data, but they lack a formal generalization framework for semantics.
– ChatGPT is a neural network that processes numbers, not words, and uses embedding and attention mechanisms to capture meaning and string together words.
– ChatGPT’s results are remarkable but not algorithmically replicating systematic human language.
– The next installment will explore the potential limitations and risks of ChatGPT and how they can be mitigated.