Extended-form Case Study

Large language models (LLMs) and financial analysis

Published on September 30, 2024   27 min

A selection of talks on Technology & Operations

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0:00
Hi. My name is Alejandro Lopez-Lira. I'm an assistant professor of finance at the University of Florida. In this lecture, we're going to be covering large language models and financial analysis.
0:15
First, we're going to start with a brief introduction to large language models.
0:21
Large language models are artificial intelligent systems trained to process, understand, and generate human language. Examples include ChatGPT, GPT-3, BERT, Transformer models, Claude, and Bard [now Gemini]. How do these models work? These models are built on deep neural networks, mimicking aspects of the human brain function. These are trained on vast corpuses of texts basically as many texts as you can get, if you can get the whole Internet, it's better, and they learn language patterns and nuances. These large language models have advanced capabilities. For example, they have contextual understanding. They can grasp subtle meaning and context in text. Moreover, they have a great generation. They can produce coherent and contextually appropriate language. These large language models represent a significant leap compared to the basic natural language processing models because they have a sophisticated understanding and a great capability of generating language.
1:28
Let's cover a little bit of history of the evolution of large language models, I promise it will not be too much. From the 1950s to the early 2000s, there was not a lot of progress. There were initial experiments with natural language processing techniques, including conceptual and rule-based system. Most of these were hard-coded rules and most of the other advancement was due to some very basic statistical models. However, thanks to deep learning, there were significant advances starting in the 2010s. For example, one key milestone was the development of the BERT model. This was a major breakthrough in 2019. This was introduced by Google and it set new standards in natural language processing benchmarks. Most of these recent models like ChatGPT and Claude and Bard are direct descendants from this type of model called BERT. This model was quickly introduced into Google search and it started influencing mainstream applications. Now beyond BERT, especially lately, there has been a significant progress towards larger and more capable models, so it turned out that most of the key to success is just having larger datasets and having more complex models.

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