Recent advances in large language models (LLMs) have revolutionized the field of artificial intelligence, particularly in finance. In this comprehensive survey, the authors focus on two crucial aspects of utilizing LLMs for financial tasks: existing solutions and guidance for adoption. The paper begins by examining current approaches that employ LLMs in finance, such as leveraging pretrained models through zero-shot or few-shot learning, fine-tuning on domain-specific data, and training custom LLMs from scratch. The authors summarize key models and evaluate their performance enhancements on various financial natural language processing tasks. Furthermore, a decision framework is proposed to assist financial professionals in selecting the most suitable LLM solution based on their specific constraints related to data availability, computational resources, and performance requirements. This framework offers a structured pathway from initial experimentation with lightweight models to potential investment in customized LLMs tailored to specific financial applications. Moreover, the paper delves into discussing the limitations and challenges associated with leveraging LLMs in financial contexts. While LLMs offer enhanced interpretability compared to traditional deep learning models by providing reasoning steps for generated answers under certain conditions, they still pose challenges in terms of explainability and governance. It is emphasized that addressing these limitations and ensuring ethical use of LLMs in finance is crucial through continuous research efforts, robust evaluation frameworks, and implementation of appropriate safeguards. In conclusion, this timely survey provides valuable insights into the emerging application of LLMs in finance. By exploring diverse approaches to harnessing LLMs for financial tasks and offering guidance on adoption considerations, the authors highlight the significant performance gains achievable with specialized LLM solutions across various natural language processing applications within the finance industry.
- Recent advances in large language models (LLMs) have revolutionized artificial intelligence, especially in finance.
- The survey focuses on two key aspects of utilizing LLMs for financial tasks: existing solutions and guidance for adoption.
- Current approaches include leveraging pretrained models through zero-shot or few-shot learning, fine-tuning on domain-specific data, and training custom LLMs from scratch.
- A decision framework is proposed to help financial professionals select the most suitable LLM solution based on constraints like data availability, computational resources, and performance requirements.
- Limitations and challenges associated with using LLMs in finance are discussed, including issues related to explainability and governance.
- Continuous research efforts, robust evaluation frameworks, and appropriate safeguards are highlighted as crucial for ensuring ethical use of LLMs in finance.
Summary- New improvements in big language models have changed how computers think, especially in money matters.
- The study looks at two important parts of using these models for money tasks: what's already there and tips for starting to use them.
- Ways people are currently using these models include using ones that were trained before, adjusting them a bit for specific jobs, or making new ones from the beginning.
- A plan is suggested to help money experts pick the best model based on things like how much data they have, how powerful their computer is, and what results they need.
- Problems and difficulties with using these models in finance are talked about, like issues with explaining how they work and following rules.
Definitions
- Large Language Models (LLMs): Advanced computer programs that understand and generate human language on a big scale.
- Pretrained models: Computer programs that have been taught lots of information before being used for a specific task.
- Domain-specific data: Information that is focused on a particular area or field, like finance or medicine.
- Constraints: Limits or restrictions that affect what can be done or chosen.
- Governance: Rules and systems put in place to control and manage something.
Recent advances in large language models (LLMs) have revolutionized the field of artificial intelligence, particularly in finance. This comprehensive survey by authors from leading academic institutions and industry experts focuses on two crucial aspects of utilizing LLMs for financial tasks: existing solutions and guidance for adoption.
The paper begins by examining current approaches that employ LLMs in finance. These include leveraging pretrained models through zero-shot or few-shot learning, fine-tuning on domain-specific data, and training custom LLMs from scratch. The authors summarize key models such as BERT, GPT-3, and XLNet, and evaluate their performance enhancements on various financial natural language processing tasks.
One major advantage of using LLMs is their ability to process vast amounts of unstructured text data with high accuracy and efficiency. This makes them well-suited for a variety of financial applications such as sentiment analysis, risk assessment, market prediction, and fraud detection. By incorporating contextual information into their predictions, LLMs can provide valuable insights for decision-making in the fast-paced world of finance.
To assist financial professionals in selecting the most suitable LLM solution based on their specific constraints related to data availability, computational resources, and performance requirements, the authors propose a decision framework. This framework offers a structured pathway from initial experimentation with lightweight models to potential investment in customized LLMs tailored to specific financial applications.
Moreover, the paper delves into discussing the limitations and challenges associated with leveraging LLMs in financial contexts. While traditional deep learning models are often considered "black boxes" due to their lack of interpretability, LLMs offer enhanced transparency by providing reasoning steps for generated answers under certain conditions. However, they still pose challenges in terms of explainability and governance when it comes to complex decisions involving sensitive financial data.
It is emphasized that addressing these limitations and ensuring ethical use of LLMs in finance is crucial through continuous research efforts. Robust evaluation frameworks and implementation of appropriate safeguards must also be in place to mitigate potential risks and biases. This is especially important given the increasing use of LLMs in financial decision-making processes.
In conclusion, this timely survey provides valuable insights into the emerging application of LLMs in finance. By exploring diverse approaches to harnessing LLMs for financial tasks and offering guidance on adoption considerations, the authors highlight the significant performance gains achievable with specialized LLM solutions across various natural language processing applications within the finance industry. As LLM technology continues to evolve, it is essential for researchers and practitioners to stay updated on its advancements and implications for the financial sector.
