Thread by @JorisTechTalk

Putting @LangChainAI to the test.

I've used LangChain and OpenAI to generate 9 questions and answers to the paper: "Eight Things to Know about Large Language Models" by @sleepinyourhat

Inspiration by @gael_duval

Code in last Tweet. Let's learn! 🧵

#AI #langchain #LLM

Question 1:

What are the eight potentially surprising claims about large language models discussed in the text?

Question 2:

How do large language models (LLMs) become more capable with increasing investment?

Question 3:

What are some important behaviors that can emerge unpredictably as a byproduct of increasing investment in LLMs?

Question 4:

How do LLMs learn and use representations of the outside world?

Question 5:

What are the techniques used to steer the behavior of LLMs, and why are they not reliable?

Question 6:

Why are experts not yet able to interpret the inner workings of LLMs?

Question 7:

How does LLM performance compare to human performance on tasks?

Question 8:

Do LLMs express the values of their creators or the values encoded in web text?

Question 9:

Why are brief interactions with LLMs often misleading?

Would love @sleepinyourhat's feedback on the provided answers.

I'll be implementing a Streamlit version of the app today.

Follow @JorisTechTalk to stay up-to-date.

Check out how it works under the hood: