Computation and Language - Evaluating LLMs on Generating Age-Appropriate Child-Like Conversations

Hey everyone, Ernis here, and welcome back to PaperLedge! Today we're diving into some fascinating research that looks at how well AI can actually talk like kids.

Think about it: Large Language Models, or LLMs – these are the brains behind things like ChatGPT – are trained on mountains of text. Mostly adult conversations, articles, and websites. But what happens when we ask them to mimic a child's way of speaking? That's precisely what this paper explores.

Researchers in Norway tackled this question head-on. They wanted to see if these LLMs could generate conversations that sound like a typical 5-year-old and a 9-year-old speaking Norwegian. This isn't just about sounding cute; it's about making AI tools that can interact effectively with children in educational settings, for example.

Now, imagine trying to teach a computer to speak like a kid. It’s kind of like trying to teach a parrot to sing opera - the parrot can mimic the sounds, but it doesn't necessarily understand the nuances, the feelings, or the context behind the music.

So, how did they do it? The researchers took five different LLMs – including big names like GPT-4 and some models specifically designed for Norwegian – and gave them the task of creating conversations suitable for 5 and 9-year-olds. Then, they did something really clever: they got eleven education professionals – teachers, child psychologists, that sort of expert – to act as judges.

These judges were given a blind test. They were shown snippets of real conversations with kids and conversations generated by the AI, all mixed together. Their job? To guess the age of the speaker.

The results were pretty interesting. Here's the key takeaway:

Even the best LLMs struggled to truly capture the authentic voice of a child. The models often produced language that sounded more advanced than what you'd expect from a 5 or 9-year-old.

It's like the AI was trying too hard, using bigger words and more complex sentences than a real child would.

Interestingly, the judges were better at guessing the age of the 5-year-olds than the 9-year-olds. This could be because the language of younger children is more distinct and predictable.

The researchers highlighted a crucial problem: data scarcity. Because the LLMs are trained on so much adult data, it’s hard to find age-appropriate Norwegian data that captures the way kids talk. So the system is already starting with a handicap.

Think of it like trying to bake a cake with only half the ingredients. You might get something resembling a cake, but it won't be quite right!

So, why does all this matter? Well, it has implications for anyone developing AI tools for children, especially in languages that aren't as widely represented online. It also raises some really interesting questions:

• Could we improve these models by training them on more data from children? How do we ethically gather that data?
• What are the potential consequences of using LLMs that don't accurately reflect a child's language development? Could it lead to misunderstandings or even hinder their learning?
• How can we ensure that AI tools designed for children are truly age-appropriate and don't inadvertently push them to speak or learn in ways that aren't natural?

This research reminds us that AI is a tool, and like any tool, it needs to be carefully designed and used responsibly. We need to think critically about the data we feed into these models and the potential impact they can have, especially on vulnerable populations like children.

That's all for this week's PaperLedge. Thanks for joining me on this exploration of AI and childhood. What do you think? Let me know in the comments!

Credit to Paper authors: Syed Zohaib Hassan, Pål Halvorsen, Miriam S. Johnson, Pierre Lison