Are ChatGPT's Good at 'Not' in QuantaMagazine
Max G. Levy, Contributing Writer, May 12, 2023
Nora Kassner suspected her computer wasn’t as smart as people thought. In October 2018, Google released a language model algorithm called BERT, which Kassner, a researcher in the same field, quickly loaded on her laptop. It was Google’s first language model that was self-taught on a massive volume of online data. Like her peers, Kassner was impressed that BERT could complete users’ sentences and answer simple questions. It seemed as if the large language model (LLM) could read text like a human (or better).
But Kassner, at the time a graduate student at Ludwig Maximilian University of Munich, remained skeptical. She felt LLMs should understand what their answers mean — and what they don’t mean. It’s one thing to know that a bird can fly. “A model should automatically also know that the negated statement — ‘a bird cannot fly’ — is false,” she said. But when she and her adviser, Hinrich Schütze, tested BERT and two other LLMs in 2019, they found that the models behaved as if words like “not” were invisible.
Since then, LLMs have skyrocketed in size and ability. “The algorithm itself is still similar to what we had before. But the scale and the performance is really astonishing,” said Ding Zhao, who leads the Safe Artificial Intelligence Lab at Carnegie Mellon University.
But while chatbots have improved their humanlike performances, they still have trouble with negation. They know what it means if a bird can’t fly, but they collapse when confronted with more complicated logic involving words like “not,” which is trivial to a human.
“Large language models work better than any system we have ever had before,” said Pascale Fung, an AI researcher at the Hong Kong University of Science and Technology. “Why do they struggle with something that’s seemingly simple while it’s demonstrating amazing power in other things that we don’t expect it to?” Recent studies have finally started to explain the difficulties, and what programmers can do to get around them. But researchers still don’t understand whether machines will ever truly know the word “no.”
Nora Kassner in a blue shirt against a black background.
Nora Kassner has tested popular chatbots and found they typically can’t understand the concept of negation.
Courtesy of Nora Kassner
Making Connections
It’s hard to coax a computer into reading and writing like a human. Machines excel at storing lots of data and blasting through complex calculations, so developers build LLMs as neural networks: statistical models that assess how objects (words, in this case) relate to one another. Each linguistic relationship carries some weight, and that weight — fine-tuned during training — codifies the relationship’s strength. For example, “rat” relates more to “rodent” than “pizza,” even if some rats have been known to enjoy a good slice.
In the same way that your smartphone’s keyboard learns that you follow “good” with “morning,” LLMs sequentially predict the next word in a block of text. The bigger the data set used to train them, the better the predictions, and as the amount of data used to train the models has increased enormously, dozens of emergent behaviors have bubbled up. Chatbots have learned style, syntax and tone, for example, all on their own. “An early problem was that they completely could not detect emotional language at all. And now they can,” said Kathleen Carley, a computer scientist at Carnegie Mellon. Carley uses LLMs for “sentiment analysis,” which is all about extracting emotional language from large data sets — an approach used for things like mining social media for opinions.
So new models should get the right answers more reliably. “But we’re not applying reasoning,” Carley said. “We’re just applying a kind of mathematical change.” And, unsurprisingly, experts are finding gaps where these models diverge from how humans read.
No Negatives.. Unlike humans, LLMs process language by turning it into math. This helps them excel at generating text — by predicting likely combinations of text — but it comes at a cost.
“The problem is that the task of prediction is not equivalent to the task of understanding,” said Allyson Ettinger, a computational linguist at the University of Chicago. Like Kassner, Ettinger tests how language models fare on tasks that seem easy to humans. In 2019, for example, Ettinger tested BERT with diagnostics pulled from experiments designed to test human language ability. The model’s abilities weren’t consistent. For example:
He caught the pass and scored another touchdown. There was nothing he enjoyed more than a good game of ____. (BERT correctly predicted “football.”)
The snow had piled up on the drive so high that they couldn’t get the car out. When Albert woke up, his father handed him a ____. (BERT incorrectly guessed “note,” “letter,” “gun.”)
And when it came to negation, BERT consistently struggled. ... '
