The past few years have seen an explosion of progress in large language model artificial intelligence systems that can do things like write poetry, conduct humanlike conversations and pass medical school exams. This progress has yielded models like ChatGPT that could have major social and economic ramifications ranging from job displacements and increased misinformation to massive productivity boosts.
Despite their impressive abilities, large language models don’t actually think. They tend to make elementary mistakes and even make things up. However, because they generate fluent language, people tend to respond to them as though they do think. This has led researchers to study the models’ “cognitive” abilities and biases, work that has grown in importance now that large language models are widely accessible.
This line of research dates back to early large language models such as Google’s BERT, which is integrated into its search engine and so has been coined BERTology. This research has already revealed a lot about what such models can do and where they go wrong.
For instance, cleverly designed experiments have shown that many language models have trouble dealing with negation – for example, a question phrased as “what is not” – and doing simple calculations. They can be overly confident in their answers, even when wrong. Like other modern machine learning algorithms, they have trouble explaining themselves when asked why they answered a certain way.
People make irrational decisions, too, but humans have emotions and cognitive shortcuts as excuses.
Words and thoughts
Inspired by the growing body of research in BERTology and related fields like cognitive science, my student Zhisheng Tang and I set out to answer a seemingly simple question about large language models: Are they rational?
Although the word rational is often used as a synonym for sane or reasonable in everyday English, it has a specific meaning in the field of decision-making. A decision-making system – whether an individual human or a complex entity like an organization – is rational if, given a set of choices, it chooses to maximize expected gain.
The qualifier “expected” is important because it indicates that decisions are made under conditions of significant uncertainty. If I toss a fair coin, I know that it will come up heads half of the time on average. However, I can’t make a prediction about the outcome of any given coin toss. This is why casinos are able to afford the occasional big payout: Even narrow house odds yield enormous profits on average.
On the surface, it seems odd to assume that a model designed to make accurate predictions about words and sentences without actually understanding their meanings can understand expected gain. But there is an enormous body of research showing that language and cognition are intertwined. An excellent example is
