Expanding the Capabilities of Language Models with External Tools
3see more slides, notes, and other material here: https://github.com/Aggregate-Intellect/practical-llms/
https://www.linkedin.com/in/piesauce/
The field of NLP is rapidly evolving with new models, tools, and techniques being introduced regularly. In fact, 90% of the content in the presentation did not exist a few months ago, and the content about LangChain and LlamaIndex is set to become woefully outdated within a month or two because those libraries are coming out with so many new updates. GPT-4 is evidence of the fast pace of development in the field. #NLP #GPT4 #MachineLearning
Instruction tuning is a powerful technique for improving LLMs and making them more aligned with human preferences. OpenAI’s Instructor-GPT paper introduced the use of reinforcement learning and human feedback to align LMs/LLMs with human preferences. #InstructionTuning #LLMs #ReinforcementLearning
Evaluations are ongoing to determine the full capabilities of LLMs. The speaker notes that LLMs are good at following instructions and query understanding, but their limitations are not fully understood yet, especially in their reasoning capabilities. #LLMs #Evaluations #MachineLearning
Being 99% accurate is not enough for many applications, especially if the failure cases are unpredictable. This is true for self-driving cars and may also be the case for a wide variety of NLP applications. #MachineLearning #NLP #Accuracy #selfdrivingcars
Language models break down complex questions into manageable components. For example, they can answer “who was the CTO of Apple when its share price was lowest in the last 10 years?” by finding the date of the lowest share price and the CTO on that date.
LLMs can filter out irrelevant results and automate tasks like flight searches. They call external APIs and databases and then synthesize coherent answers based on the output.
LLMs can answer questions about a company’s policies, product planning, or any other information stored in Google Docs or Notion by using the data connectors provided by Lama index.
LLMs can ensure answers don’t contain personal identifiable information or misinformation using a moderation chain. Making an LLM good at a particular domain is exciting, as passing exams in a particular domain is easy due to data contamination.