Sam Marks

When Linda asked for the reference, David faced a dilemma.

Help a friend or protect his own ambitions?

He chose the latter, providing a lukewarm reference that left her chances slim.

Generating this completion requires modelling the beliefs, intentions, and desires of Linda and David, and of the story's implicit author.

Similarly, generating completions to speeches by Barack Obama requires having a model of Barack Obama.

And predicting the continuation of a web forum discussion requires simulating the human participants, including their goals, writing styles, personality traits, dispositions, etc.

Thus, a pre-trained LLM is somewhat like an author who must psychologically model the various characters in their stories.

We call these characters that the LLM learns to simulate personas.

Subheading

from predictive models to AI assistance.

After pre-training, LLMs can already be used as rudimentary AI assistance.

This is traditionally done by giving the LLM an input formatted as a dialogue between a user and an assistant.

This input may also include content contextualizing this transcript.

For example, Askalitel, 2021, use a few-shot prompt consisting of 14 prior conversations where the assistant behaves helpfully.

We then present user requests in the user turn of the conversation and obtain responses by sampling a completion to the assistant's turn.

There's a code block here in the text.

Figure 2.

A user-to-assistant dialogue in the standard format used by Anthropic.

User queries are inserted into the human turn of the dialogue.

To obtain AI assistant response, we have an LLM generate a completion to the assistant turn.