Large Language Models Often Say One Thing and Do Another

CHAPTER 7 Large Language Models "How much do we know at any time? Much more, or so I believe, than we know we know." Agatha Christie, The Moving Finger The literature of the fantastic abounds in inanimate objects magically endowed with the gift of speech. From Ovid's statue of Pygmalion to Mary Shelley's story about Frankenstein, we continually reinvent stories about creating something and then having a chat with it. Legend has it that after finishing his sculpture Moses, Michelangelo thought it so lifelike that he tapped it on the knee and commanded it to speak. Perhaps this shouldn't be surprising. Language is the mark of humanity and sentience. conversation is the most fundamental arena of language, the first kind of language we learn as children, and the kind we engage in constantly, whether we are teaching or learning, ordering lunch, or talking with our families or friends. This chapter introduces the Large Language Model, or LLM, a computational agent that can interact conversationally with people. The fact that LLMs are designed for interaction with people has strong implications for their design and use. Many of these implications already became clear in a computational system from 60 years ago, ELIZA (Weizenbaum, 1966) . ELIZA, designed to simulate a Rogerian psychologist, illustrates a number of important issues with chatbots. For example people became deeply emotionally involved and conducted very personal conversations, even to the extent of asking Weizenbaum to leave the room while they were typing. These issues of emotional engagement and privacy mean we need to think carefully about how we deploy language models and consider their effect on the people who are interacting with them. In this chapter we begin by introducing the computational principles of LLMs; we'll discuss their implementation in the transformer architecture in the following chapter. The central new idea that makes LLMs possible is the idea of pretraining, so let's begin by thinking about the idea of learning from text, the basic way that LLMs are trained. We know that fluent speakers of a language bring an enormous amount of knowledge to bear during comprehension and production. This knowledge is embodied in many forms, perhaps most obviously in the vocabulary, the rich representations we have of words and their meanings and usage. This makes the vocabulary a useful lens to explore the acquisition of knowledge from text, by both people and machines. Estimates of the size of adult vocabularies vary widely both within and across languages. For example, estimates of the vocabulary size of young adult speakers of American English range from 30,000 to 100,000 depending on the resources used