Episode 28: Beyond Supervised Learning: The Rise of In-Context Learning with LLMs
Jun 9, 2024
auto_awesome
Alan Nichol, Co-founder and CTO of Rasa, shares insights on using LLMs in chatbots, the evolution of conversational AI, and the challenges of supervised learning. He emphasizes the importance of balancing traditional techniques with new advancements. The podcast also includes a live demo of Rasa's CALM system, showcasing the separation of business logic from language models for reliable conversational flow execution.
Implementing LLMs in chatbots necessitates balancing autonomy with business logic for secure actions like money transfers.
Understanding the historical progression of conversational AI aids in effective LLM integration for future chatbot development.
Incorporating LLMs into chatbots should focus on interpreting context within predefined business logic to ensure reliable conversation outcomes.
Deep dives
Challenges of Using Large Language Models in Conversational AI
Incorporating LLMs into chatbots raises questions on whether to trust a single large LLM like GPT or secure its actions within distinct business logic. The episode explores how to effectively implement LLMs in chatbots for actions like secure money transfers.
The Evolution of Chatbots and Conversational AI
The podcast episode delves into the history of conversational AI pre-LLMs, highlighting early rule-based chatbots and the progression towards using LLMs. It emphasizes the importance of understanding the historical context to inform future chatbot development.
Balancing Language Models with Business Logic in Conversational AI
The episode discusses the approach of using LLMs primarily for understanding instead of full autonomy in chatbots. It presents a methodology called Calm for leveraging LLMs to interpret meaning in context while executing predefined business logic, aiming to ensure reliable conversation outcomes.
Shift in Focus to Data for Improving ML Models
The progress in machine learning technologies like model fitting and hyperparameter search led to a shift in focus from model convergence to evaluating if the model solves the actual problem. However, the education in the field failed to emphasize the importance of data sets and data quality in the model-building process. Many practitioners neglect the significance of working on data sets beyond model fitting, leading to failures and challenges in supervised learning systems.
Importance of Business Logic and Flow Structure in LLM Development
In the context of developing conversational AI using large language models (LLMs), maintaining a clear separation between business logic and conversation paths is crucial. By focusing on business logic within declarative flow structures, developers can ensure the faithful execution of task steps without relying on complex conversation models. This approach streamlines LLM usage, emphasizing efficiency and effectiveness in conversational AI development.
Hugo speaks with Alan Nichol, co-founder and CTO of Rasa, where they build software to enable developers to create enterprise-grade conversational AI and chatbot systems across industries like telcos, healthcare, fintech, and government.
What's super cool is that Alan and the Rasa team have been doing this type of thing for over a decade, giving them a wealth of wisdom on how to effectively incorporate LLMs into chatbots - and how not to. For example, if you want a chatbot that takes specific and important actions like transferring money, do you want to fully entrust the conversation to one big LLM like ChatGPT, or secure what the LLMs can do inside key business logic?
In this episode, they also dive into the history of conversational AI and explore how the advent of LLMs is reshaping the field. Alan shares his perspective on how supervised learning has failed us in some ways and discusses what he sees as the most overrated and underrated aspects of LLMs.
Alan offers advice for those looking to work with LLMs and conversational AI, emphasizing the importance of not sleeping on proven techniques and looking beyond the latest hype. In a live demo, he showcases Rasa's Calm (Conversational AI with Language Models), which allows developers to define business logic declaratively and separate it from the LLM, enabling reliable execution of conversational flows.