Each Sunday, NPR host Will Shortz, The New York Occasions’ crossword puzzle guru, will get to quiz hundreds of listeners in a long-running phase known as the Sunday Puzzle. Whereas written to be solvable with out too a lot foreknowledge, the brainteasers are normally difficult even for expert contestants.
That’s why some specialists suppose they’re a promising option to check the boundaries of AI’s problem-solving skills.
In a recent study, a group of researchers hailing from Wellesley School, Oberlin School, the College of Texas at Austin, Northeastern College, Charles College, and startup Cursor created an AI benchmark utilizing riddles from Sunday Puzzle episodes. The group says their check uncovered stunning insights, like that reasoning fashions — OpenAI’s o1, amongst others — typically “hand over” and supply solutions they know aren’t appropriate.
“We wished to develop a benchmark with issues that people can perceive with solely normal data,” Arjun Guha, a pc science college member at Northeastern and one of many co-authors on the research, informed TechCrunch.
The AI business is in a little bit of a benchmarking quandary for the time being. A lot of the assessments generally used to judge AI fashions probe for expertise, like competency on PhD-level math and science questions, that aren’t related to the typical person. In the meantime, many benchmarks — even benchmarks released relatively recently — are shortly approaching the saturation level.
The benefits of a public radio quiz recreation just like the Sunday Puzzle is that it doesn’t check for esoteric data, and the challenges are phrased such that fashions can’t draw on “rote reminiscence” to unravel them, defined Guha.
“I believe what makes these issues laborious is that it’s actually troublesome to make significant progress on an issue till you resolve it — that’s when every little thing clicks collectively ,” Guha stated. “That requires a mixture of perception and a technique of elimination.”
No benchmark is ideal, in fact. The Sunday Puzzle is U.S. centric and English solely. And since the quizzes are publicly accessible, it’s attainable that fashions educated on them can “cheat” in a way, though Guha says he hasn’t seen proof of this.
“New questions are launched each week, and we will count on the newest inquiries to be really unseen,” he added. “We intend to maintain the benchmark contemporary and observe how mannequin efficiency modifications over time.”
On the researchers’ benchmark, which consists of round 600 Sunday Puzzle riddles, reasoning fashions corresponding to o1 and DeepSeek’s R1 far outperform the remaining. Reasoning fashions totally fact-check themselves earlier than giving out outcomes, which helps them keep away from a number of the pitfalls that usually journey up AI fashions. The trade-off is that reasoning fashions take a bit longer to reach at options — sometimes seconds to minutes longer.
At the very least one mannequin, DeepSeek’s R1, offers options it is aware of to be mistaken for a number of the Sunday Puzzle questions. R1 will state verbatim “I hand over,” adopted by an incorrect reply chosen seemingly at random — habits this human can definitely relate to.
The fashions make different weird decisions, like giving a mistaken reply solely to instantly retract it, try and tease out a greater one, and fail once more. In addition they get caught “pondering” endlessly and provides nonsensical explanations for solutions, or they arrive at an accurate reply instantly however then go on to think about various solutions for no apparent motive.
“On laborious issues, R1 actually says that it’s getting ‘pissed off,’” Guha stated. “It was humorous to see how a mannequin emulates what a human may say. It stays to be seen how ‘frustration’ in reasoning can have an effect on the standard of mannequin outcomes.”
The present best-performing mannequin on the benchmark is o1 with a rating of 59%, adopted by the just lately launched o3-mini set to excessive “reasoning effort” (47%). (R1 scored 35%.) As a subsequent step, the researchers plan to broaden their testing to further reasoning fashions, which they hope will assist to determine areas the place these fashions is likely to be enhanced.
“You don’t want a PhD to be good at reasoning, so it must be attainable to design reasoning benchmarks that don’t require PhD-level data,” Guha stated. “A benchmark with broader entry permits a wider set of researchers to grasp and analyze the outcomes, which can in flip result in higher options sooner or later. Moreover, as state-of-the-art fashions are more and more deployed in settings that have an effect on everybody, we consider everybody ought to be capable to intuit what these fashions are — and aren’t — able to.”