How do LLMs benefit FinTech & Financial Industries?

This is a good summary of LLM (Large Language Model) adoption in the FinTech and Financial worlds

There have been several use cases over the years, including:

🔹 Sentiment Analysis

🔹 Text Classification

🔹 Question Answering

🔹 Stock Movement Prediction

🔹 Name Entity Recognition

🔹 Text Summarization

When a company decides to leverage LLMs to benefit the business, it is important to evaluate budget and data requirements to check which level of adoption fits the company.

🔹 Level 1 - Zero-shot

Internal confidential data doesn't fit well for 3rd-party LLMs. Zero-shot methods fit better in this case, which can perform tasks it hasn't been explicitly trained on.

🔹 Level 2 - Few-shot

If we can provide some examples in the prompt for the LLM to learn, the LLM can leverage the examples to guide the model in understanding and performing a new task. This is called the few-shot method.

🔹 Level 3 - Fine-tuning

People talk about fine-tuning like a silver bullet, but the lack of domain-specific data or tools makes fine-tuning impossible.

🔹 Level 4 - From-scratch training

Training an LLM from scratch with corpus is not an economic approach. A company needs to evaluate the budget for computing power and human resources before going down this route.

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