Fine-Tuning LLMs For ‘Good’ Behavior Makes Them More Likely To Say No

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. 

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.