About Course
Workshop Overview
Day 1:
Exploring Generative AI and Large Language Models (LLMs) in Asset Management
- In depth look, into the applications of LLMs such as BARD, ChatGPT in the industry.
- Utilizing LLMs to develop trading approaches.
- Practical session; Converting data into signals for high frequency trading.
Day 2:
Advanced Strategies and Real World Uses
- Informal Conversation with Lisa Huang
- Discussion, on design and methods to manage risks associated with LLMs.
- Enhancing trading tactics with detailed sentiment analysis through LLMs.
- Hands on activity; Testing trading strategies and exploring how LLMs could transform asset management practices.
Workshop Outline
01 Exploring Big Language Models (BLMs) & Pre trained Generative Transformers (PGT)
- Getting familiar, with BLMs like BARD, ChatGPT and other advanced language models
- Common Uses of BLMs
- Understanding the functionality of BLMs
- Accessing BARD/PaLM online using their API
02 Developing Software
- Introduction to Prompt Design
- Creating software, for tasks like writing text summarizing content and more.
- Exploring few shot learning, with BARD
- Introduction to embeddings and their significance
- An overview of the BARD embeddings API. How it is utilized
03 Risks Linked with Language Models (LLMs)
- Recognizing risks associated with LLMs, including hallucinations, bias, consent and security.
- Strategies, for mitigating the risk of hallucinations, such as retrieval enhancement, prompt manipulation and self analysis.
- Techniques for identifying and managing hallucinations, including reinforcement learning based on feedback (RLHF) and model driven approaches.
04 Utilizing Language Models for Analyzing Federal Reserve Chairs Speeches
- Reasons for selecting the BARD family over LLMs.
- Assessment of BARDs performance.
- Enhancing performance through embeddings.
- Practical demonstration; evaluating sentiment scores on companies using embeddings.
- Test data; Video recordings of the Federal Reserve Chairs press conferences.
- Conducting an analysis of a trading strategy based on the sentiment analysis provided by an LLM.
05 Implementation of Language Models in Real world Scenarios
- Practices for deploying LLMs in production environments.
- Overview of models, like ChatGPT, BART, Cohere, Alpaca, etc.
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