If you have questions like:
- Why do language models work so well?
- What are the biggest bottlenecks for current large language models?
- How can we improve future language models?
I’d strongly recommend watching this, I just did and it was really great
Summary of Video
This video is about understanding why language models work so well. It is given by Jason Wei, a researcher at OpenAI, at Stanford CS25 class. The speaker says that manually inspecting data is a very helpful way to understand how well language models work. He gives an example of his own experience where he trained a model to classify lung cancer by reading research papers and consulting with a pathologist.The video then dives into the details of how language models work. The speaker explains that these models are trained on a massive amount of text data with the task of predicting the next word in a sequence. This forces the model to learn many different things, including grammar, vocabulary, and factual knowledge. The speaker argues that this is a form of multi-tasking learning.
Another important factor that affects how well these models perform is the amount of compute power that is available for training. The speaker shows that as the amount of compute power increases, the performance of the model also increases. This is because the model is able to learn more complex patterns from the data.
The video then discusses some of the challenges of interpreting how these models work. The speaker gives an example of a prompt that asks the model to repeat a phrase, fix a quote, and follow an instruction. The model performs well on this task when it is given enough compute power, but it is difficult to understand exactly how it is able to do this.
In conclusion, the speaker argues that the success of language models is due to a combination of factors, including the massive amount of data that they are trained on, the amount of compute power that is available, and the complex way that they are able to learn from this data.