A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges (2024)

FAQs

What are the future applications of large language models? ›

Their capability to translate content across various contexts will expand further, making them usable by business users of all levels of technical expertise. Allowing more accurate data via domain-oriented LLMs developed for specific sectors or functions is another direction for the upcoming large language models.

Large Language Models (LLMs) have revolutionized various sectors, including education, healthcare, business, and creative industries. They enhance learning experiences, improve accessibility, and streamline assessment processes, allowing educators to focus on more nuanced teaching.

LLMs in banking help institutions consider different scenarios and assess the associated risks. They analyze historical data and market trends to flag potential issues. One of the most valuable use cases involves creating a detailed customer profile based on a customer's transaction history and online behavior.

A large language model (LLM) is a deep learning algorithm that can perform a variety of natural language processing (NLP) tasks. Large language models use transformer models and are trained using massive datasets — hence, large. This enables them to recognize, translate, predict, or generate text or other content.

In 2019, the largest LLM had parameters of 0.09 billion. This figure increased to 17.2 billion in 2020 before exploding to 540 billion in 2022. Over a few years, a tiny figure grew exponentially, achieving a 574,368% increase.

LLMs can be utilized alongside generative AI models to improve content translation and localization. A large language model can decipher the nuances of language, while generative AI can create accurate translations and localized versions of the content.

OpenAI's large language models, including the models that power ChatGPT, are developed using three primary sources of information: (1) information that is publicly available on the internet, (2) information that we license from third parties, and (3) information that our users or our human trainers provide.

The authors found the LLMs to be prone to similar content effects as humans. Both humans and LLMs are more likely to mistakenly label an invalid argument as valid when the semantic content is sensical and believable.

Financial institutions can now use large language models to analyze mass data volumes, such as financial reports, and quickly generate summaries and insights that aid in risk assessment.

What is LLM best for? ›

The LLM was created for lawyers to expand their knowledge, study a specialized area of law, and gain international qualifications if they have earned a law degree outside the U.S. or Canada. If you're looking to advance your legal career or take the next step in your academic journey, an LLM could be for you.

The importance of LLM in Natural Language Processing (NLP)

They possess an extensive understanding of general language patterns and knowledge based on massive data sets. This enables them to achieve superior results on various tasks, such as question answering, information retrieval, sentiment analysis, and more.

Large language models (LLMs) are machine learning models that can comprehend and generate human language text. They work by analyzing massive data sets of language.

Ernie is Baidu's large language model which powers the Ernie 4.0 chatbot. The bot was released in August 2023 and has garnered more than 45 million users. Ernie is rumored to have 10 trillion parameters. The bot works best in Mandarin but is capable in other languages.

They allow the model to relate words together through attention to multiple words in a text sequence. LLMs have been shown to be highly effective for a range of tasks in natural language processing (NLP), including classification and information extraction tasks and generative applications.

Key Impact

Future LLMs will understand and generate text, code, and images. Future LLMs will explain their reasoning and build trust. LLMs make programming more accessible for beginners. LLMs streamline communication and code review in group projects.

Researchers have made rapid progress extending LLM context length and augmenting their memory in other ways. They are also improving LLMs' reasoning capabilities through orchestration, or creating dynamic workflows that help the agent decompose the task into sub-tasks, and formulate, test, and execute a plan.

Education And Training: LWMs have the potential to transform the educational landscape by providing highly personalized learning experiences. These models could adapt to individual learning styles and paces, offering customized educational content that evolves based on student performance and engagement.