Can Machines Solve Analogies Like Kids?
Exploring LLMs' struggles with analogical reasoning compared to children and adults.
Claire E. Stevenson, Alexandra Pafford, Han L. J. van der Maas, Melanie Mitchell
― 4 min read
Table of Contents
When you think about kids figuring out puzzles like "body : feet :: table : ?", you might wonder if machines, like large language models (LLMs), can do the same thing. Children learn at a young age how to take what they know from one example and apply it to another. They see patterns and can solve analogies, connecting dots that sometimes even stump adults. Recent studies suggest that while LLMs can tackle certain analogy problems, they struggle to generalize their problem-solving skills across different contexts as well as children do.
Analogical Reasoning?
What isAnalogical reasoning is when you use what you already know about one thing to understand another situation. For example, if you know that a body has feet, you can figure out that a table has legs. It’s a fundamental skill that helps humans learn and think creatively. Adults often outperform children on these tasks, but surprisingly, kids can solve simple analogies as young as three or four. They can switch from one analogy type to another quite easily, which is not something LLMs are particularly good at, as shown in recent research.
The Study
In our study, we wanted to see if LLMs could generalize their analogy-solving skills the same way kids and adults can. We asked kids, adults, and LLMs to work on letter-string analogies. These analogies are based on the Latin, Greek, and even a made-up symbol list to test how well both humans and machines transfer their knowledge to new contexts.
Letter-String Analogies
The letter-string analogy task works like this: if you have "abc" that changes to "abd," what should "pqr" change to? Similar changes need to be made to solve the puzzle. This type of task is straightforward and relies on basic letter transformations that humans usually get right, as they can easily identify and apply patterns.
Who Participated?
We had 42 children aged 7-9, 62 adults, and we ran tests on four different LLMs. All participants were given the same set of tasks across three types of alphabets: Latin, Greek, and Symbols.
How Did Everyone Perform?
Adults and Kids vs. LLMs
Our predictions were that adults and kids would handle the Latin alphabet with ease, and we thought LLMs would keep up with adults. While many LLMs performed well with the Latin alphabet, they faltered when it came to the Greek alphabet, and their Performance dropped significantly with the Symbol list. This showed a key difference: while adults and kids adapted well, LLMs struggled to adapt when things became less familiar.
Overall Results
When comparing performance across the different alphabets, both kids and adults showed similar results, performing consistently well. However, LLMs had a harder time. It was clear that their ability to grasp rules and apply them flexibly was lacking when they faced changes in the types of letters or symbols.
Why Can’t LLMs Generalize Like Kids?
The Hard Parts
To understand why LLMs found it hard to generalize, we looked closely at the tasks. It turned out that the more complex rules, like recognizing the order of letters, were the toughest for LLMs to follow. They did much better with simpler tasks but struggled with items that required a more nuanced understanding of patterns.
Doing a Rule Check
We tried a simpler version of the task, only focusing on specific rules like "the next letter" or "the previous letter." The LLMs managed to get these right in a straightforward list, but when we switched back to analogies that required them to mix and match those rules, they faltered again. This hints that LLMs excel at identifying patterns when conditions are right but don’t translate that ability well to more abstract tasks.
Errors Did They Make?
WhatWhen we analyzed the errors made by kids, adults, and LLMs, we saw clear differences. Kids sometimes strayed far from the correct answers, while LLMs tended to follow a more predictable pattern of wrong answers. Interestingly, LLMs often relied on a "literal" interpretation of rules, while humans didn't. This shows they apply learned rules rigidly, which can limit flexibility.
Conclusion
In summary, while LLMs can solve simple letter-string analogies, their ability to generalize across different contexts is not on par with children. It highlights a limitation in their reasoning abilities compared to humans. The ability to adapt and apply knowledge to new situations seems to be a uniquely human trait, indicating that we still have a way to go before machines can think like us. So next time you see a child figure out a puzzle, remember, their brains are doing something machines are still trying to catch up with!
Title: Can Large Language Models generalize analogy solving like people can?
Abstract: When we solve an analogy we transfer information from a known context to a new one through abstract rules and relational similarity. In people, the ability to solve analogies such as "body : feet :: table : ?" emerges in childhood, and appears to transfer easily to other domains, such as the visual domain "( : ) :: < : ?". Recent research shows that large language models (LLMs) can solve various forms of analogies. However, can LLMs generalize analogy solving to new domains like people can? To investigate this, we had children, adults, and LLMs solve a series of letter-string analogies (e.g., a b : a c :: j k : ?) in the Latin alphabet, in a near transfer domain (Greek alphabet), and a far transfer domain (list of symbols). As expected, children and adults easily generalized their knowledge to unfamiliar domains, whereas LLMs did not. This key difference between human and AI performance is evidence that these LLMs still struggle with robust human-like analogical transfer.
Authors: Claire E. Stevenson, Alexandra Pafford, Han L. J. van der Maas, Melanie Mitchell
Last Update: Nov 4, 2024
Language: English
Source URL: https://arxiv.org/abs/2411.02348
Source PDF: https://arxiv.org/pdf/2411.02348
Licence: https://creativecommons.org/licenses/by-nc-sa/4.0/
Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.
Thank you to arxiv for use of its open access interoperability.