Fixing "goldfish memory" with GPT-3 and external sources of information in a chatbot - part 1 | Summary and Q&A

TL;DR

This video discusses the challenges of integrating new information and long-term memory into GPT-3 chatbots and proposes a solution using external sources like Wikipedia.

Key Insights

• 👶 The need for conversational chatbots to handle long-term memory and new information integration.
• 🍉 The challenges posed by GPT-3's static nature and limited long-term memory capacity.
• ℹ️ The solution of querying Wikipedia as a live source of external information.
• 🥠 The importance of a fine-tuned model and multi-document answering system for enhanced conversational capabilities.
• 👻 The "make_conversation" function allows users to interact with the chatbot and access relevant information.
• 🫥 The utilization of indexing to search and retrieve conversation lines.
• 👨‍🔬 The significance of prompt engineering to generate relevant follow-up questions and search queries.

Transcript

morning everybody david shapiro here um i'm doing the rare recording two videos back to back because um i woke up with inspiration woke up at five in the morning and was ready to go okay so the reason that you're here um this dude uh ravi he asked a good question and he put it so succinctly that i need to borrow his language so thank you ravi for a... Read More

Questions & Answers

Q: How does GPT-3 handle new information and long-term memory?

GPT-3 struggles with integrating new information as it was only trained on data up to 2021. Additionally, it has limited long-term memory, often referred to as "goldfish memory."

Q: How does the "Long-Term Chat with External Sources" solution address these challenges?

The solution involves querying Wikipedia, a live source of external information, to obtain up-to-date data. It also utilizes a fine-tuned model and a multi-document answering system to enhance conversational capabilities.

Q: What is the purpose of the "make_conversation" function?

The "make_conversation" function allows users to engage in a chat with the chatbot. It accumulates the conversation, searches indexed data, and responds accordingly.

Q: How does the new repository solve the problem of long-term memory and new information integration?

By querying Wikipedia, the chatbot can access current information. The conversation is indexed to allow for searching and retrieval of relevant lines. The fine-tuned model and multi-document answering system help generate accurate responses.

Summary & Key Takeaways

• The video addresses the need to develop a conversational app for school kids using a friendly chatbot that can handle a wide range of topics.

• The two primary challenges identified are the static nature of GPT-3 and its limited long-term memory capacity.

• The presenter introduces a new repository called "Long-Term Chat with External Sources" to tackle these challenges by querying Wikipedia for up-to-date information and using a fine-tuned model for generating conversational responses.