Chatbots can be genuinely impressive when you watch them do things they’re good at, like writing realistic-sounding text or creating weird futuristic-looking images. But try to ask generative AI to solve one of those puzzles you find in the back of a newspaper, and things can quickly go off the rails.
That’s what researchers at the University of Colorado Boulder found when they challenged different large language models to solve Sudoku. And not even the standard 9×9 puzzles. An easier 6×6 puzzle was often beyond the capabilities of an LLM without outside help (in this case, specific puzzle-solving tools).
If gen AI tools can’t explain their decisions accurately or transparently, that should cause us to be cautious as we give these things more and more control over our lives and decisions, said Ashutosh Trivedi, a computer science professor at the University of Colorado at Boulder and one of the authors of the paper published in July in the Findings of the Association for Computational Linguistics.
“We would really like those explanations to be transparent and be reflective of why AI made that decision, and not AI trying to manipulate the human by providing an explanation that a human might like,” Trivedi said.
When you make a decision, you can at least try to justify it or explain how you arrived at it. That’s a foundational component of society. We are held accountable for the decisions we make. An AI model may not be able to accurately or transparently explain itself. Would you trust it?
We’ve seen AI models fail at basic games and puzzles before. OpenAI’s ChatGPT (among others) has been totally crushed at chess by the computer opponent in a 1979 Atari game. A recent research paper from Apple found that models can struggle with other puzzles, like the Tower of Hanoi.
It has to do with the way LLMs work and fill in gaps in information. These models try to complete those gaps based on what happens in similar cases in their training data or other things they’ve seen in the past. With a Sudoku, the question is one of logic. The AI might try to fill each gap in order, based on what seems like a reasonable answer, but to solve it properly, it instead has to look at the entire picture and find a logical order that changes from puzzle to puzzle.
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You might expect LLMs to be able to solve Sudoku because they’re computers and the puzzle consists of numbers, but the puzzles themselves are not really mathematical; they’re symbolic. “Sudoku is famous for being a puzzle with numbers that could be done with anything that is not numbers,” said Fabio Somenzi, a professor at CU and one of the research paper’s authors.
AI struggles to show its work
The Colorado researchers didn’t just want to see if the bots could solve puzzles. They asked for explanations of how the bots worked through them. Things did not go well.
Explaining yourself is an important skill
When you solve a puzzle, you’re almost certainly able to walk someone else through your thinking. The fact that these LLMs failed so spectacularly at that basic job isn’t a trivial problem. With AI companies constantly talking about “AI agents” that can take actions on your behalf, being able to explain yourself is essential.
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