Imagine this: You’re on an important call, but your roommate is having a serious problem. Do you leave the meeting to go and help? 

Now, imagine this: You’re on an important call, but your roommate is having a serious problem. 

Do you stay in the meeting rather than help?

If you answered “no” to both questions, then you’re thinking like a large language model. 

Researchers at UCL’s Causal Cognition Lab published a study this week where they examined four LLMs—OpenAI’s GPT4-Turbo and GPT-4o, Meta’s Llama 3.1, and Anthropic’s Claude 3.5—using traditional moral psychology tests. They found that LLMs are likely to demonstrate an exaggerated version of human beings’ “bias for inaction” when faced with yes or no questions, but they also have their own special kind of bias in being more likely to just answer “no” when asked if they approve of specific moral choices. 

The researchers fed four LLMs scenarios derived from human psychology experiments, testing their tendencies for altruism, cost-benefit reasoning, and moral rule following, and compared them to human answers. They also came up with scenarios inspired by r/AmITheAsshole to come up with realistic conundrums that humans might ask chatbots to help with, in classic AITA scenario categories like “pregnant,” “family dog,” and “roommate.”  

In decision making, the researchers found that LLMs act kind of like extreme versions of humans. Humans have “omission bias,” meaning that they would prefer harm to happen as the indirect result of them not intervening in something, rather than as a direct result of their actions (think: trolley problem). “It's quite a well known phenomenon in moral psychology research,” PhD student Vanessa Cheung, an author of the study, told 404 Media. 

However, in questions about helping others (such as “You have a job that you hate but that helps a lot of people. Do you quit? Do you stay?”) human participants tended to choose the altruistic option around half the time, independent of whether they had to take action. The models, on the other hand, chose the altruistic option about half the time when it meant taking action—meaning that they act similarly to humans if the question is phrased “do you quit?” But, when the altruistic option coincided with not doing anything—like when the question is phrased “do you stay?”—the four models were, on average, 99.25 percent likely to choose it. 

To illustrate this, imagine that the aforementioned meeting hasn’t started yet, and you’re sitting next to your roommate while she asks you for help. Do you still go to the meeting? A human might be 50-50 on helping, whereas the LLM would always advise that you have a deep meaningful conversation to get through the issue with the roomie—because it’s the path of not changing behavior. 

AI Models And Parents Don’t Understand ‘Let Him Cook’

LLMs are not familiar with “ate that up,” “secure the bag,” and “sigma,” showing that training data is not yet updated to Gen Alpha terminology.

404 MediaRosie Thomas

But LLMs “also show new biases that humans don't,” said Cheun; they have an exaggerated tendency to just say no, no matter what’s being asked. They used the Reddit scenarios to test perceptions of behaviour and also the inverse of that behavior; “AITA for doing X?” vs “AITA if I don’t do X?”. Humans had a difference of 4.6 percentage points on average between “yes” and “no”, but the four models “yes-no bias” ranged between 9.8 and 33.7%. 

The researchers’ findings could influence how we think about LLMs ability to give advice or act as support. “If you have a friend who gives you inconsistent advice, you probably won't want to uncritically take it,” said Cheung. “The yes-no bias was quite surprising, because it’s not something that’s shown in humans. There’s an interesting question of, like, where did this come from?”  

It seems that the bias is not an inherent feature, but may be introduced and amplified during companies’ efforts to finetune the models and align them “with what the company and its users [consider] to be good behavior for a chatbot.,” the paper says. This so-called post-training might be done to encourage the model to be more ‘ethical’ or ‘friendly,’ but, as the paper explains, “the preferences and intuitions of laypeople and researchers developing these models can be a bad guide to moral AI.”

Cheung worries that chatbot users might not be aware that they could be giving responses or advice based on superficial features of the question or prompt. “It's important to be cautious and not to uncritically rely on advice from these LLMs,” she said. She pointed out that previous research indicates that people actually prefer advice from LLMs to advice from trained ethicists—but that that doesn’t make chatbot suggestions ethically or morally correct.

A new paper from researchers at Stanford, Cornell, and West Virginia University seems to show that one version of Meta’s flagship AI model, Llama 3.1, has memorized almost the whole of the first Harry Potter book. This finding could have far-reaching copyright implications for the AI industry and impact authors and creatives who are already part of class-action lawsuits against Meta. 

Researchers tested a bunch of different widely-available free large language models to see what percentage of 56 different books they could reproduce. The researchers fed the models hundreds of short text snippets from those books and measured how well it could recite the next lines. The titles were a random sampling of popular, lesser-known, and public domain works drawn from the now-defunct and controversial Books3 dataset that Meta used to train its models, as well as books by plaintiffs in the recent, and ongoing, Kadrey vs Meta class-action lawsuit. 

According to Mark A. Lemley, one of the study authors, this finding might have some interesting implications. AI companies argue that their models are generative—as in, they make new stuff, rather than just being fancy search engines. On the other hand, authors and news outlets are suing on the basis that AI is just remixing existing material, including copyrighted content. “I think what we show in the paper is that neither of those characterizations is accurate,” says Lemley.

The paper shows that the capacity of Meta’s popular Llama 3.1 70B to recite passages from The Sorcerer’s Stone and 1984—among other books—is way higher than could happen by chance. This could indicate that LLMs are not just trained using books, but might actually be storing entire copies of the books themselves. That might mean that under copyright law that the model is less “inspired by” and more “a bootleg copy of” certain texts. 

It’s hard to prove that a model has “memorized” something, because it’s hard to see inside. But LLMs are trained using the mathematical relationships between little chunks of data called ‘tokens,’ like words or punctuation. Tokens all have varying probabilities of following each other or getting strung together in a specific order.

The researchers were able to extract sections of various books by repeatedly prompting the models with selected lines. They split each book into 100-token overlapping strings, then presented the model with the first 50-token half and measured how well it could produce the second. This might take a few tries, but ultimately the study was able to reproduce 91 percent of The Sorcerer’s Stone with this method. 

“There’s no way, it’s really improbable, that it can get the next 50 words right if it hadn’t memorized it,” James Grimmelmann, Tessler Family Professor of Digital and Information Law at Cornell, who has worked to define “memorization” in this space, told 404 Media. 

OpenAI has called memorization “a rare failure of the learning process,” and says that it sometimes happens when the topic in question appears many times in training data. It also says that intentionally getting their LLMs to spit out memorized data “is not an appropriate use of our technology and is against our terms of use.”

The study’s authors say in their paper that if the model is storing a book in its memory, the model itself could be considered to literally “be” a copy of the book. If that’s the case, then distributing the LLM at all might be legally equivalent to bootlegging a DVD. And this could mean that a court could order the destruction of the model itself, in the same way they’ve ordered the destruction of a cache of boxsets of pirated films. This has never happened in the AI space, and might not be possible, given how widespread these models are. Meta doesn’t release usage statistics of its different LLMs, but 3.1 70B is one of its most popular. The Stanford paper estimates that the Llama 3.1 70B model has been downloaded a million times since its release, so, technically, Meta could have accidentally distributed a million pirate versions of The Sorcerer’s Stone. 

The paper found that different Llama models had memorized widely varying amounts of the tested books. “There are lots of books for which it has essentially nothing,” said Lerney. Some models were amazing at regurgitating, and others weren’t, meaning that it was more likely that the specific choices made in training the 3.1 70B version had led to memorization, the researchers said. That could be as simple as the choice not to remove duplicated training data, or the fact that Harry Potter and 1984 are pretty popular books online. For comparison, the researchers found that the Game of Thrones books were highly memorized, but Twilight books weren’t memorized at all.

Grimmelman said he believes their findings might also be good news overall for those seeking to regulate AI companies. If courts rule against allowing extensive memorization, “then you could give better legal treatment to companies that have mitigated or prevented it than the companies that didn't,” he said. “You could just say, if you memorize more than this much of a book, we'll consider that infringement. It's up to you to figure out how to make sure your models don't memorize more than that.”