Computation and Language - DeduCE Deductive Consistency as a Framework to Evaluate LLM Reasoning

Alright PaperLedge crew, Ernis here, ready to dive into some fascinating research! Today, we're tackling a paper that asks a really important question: are those super-smart AI language models actually understanding math, or are they just really good at memorizing and regurgitating answers?

You know, these big language models, they can ace those super tough Olympiad math problems. It's like watching a grandmaster chess player – impressive! But what happens when you throw them a curveball, a high school math problem they haven't seen before? Suddenly, they can stumble. And that's what this paper digs into.

Instead of just looking at whether the AI gets the final answer right or wrong, these researchers are doing a deep dive into the reasoning process itself. They're using something called a "deductive consistency metric." Think of it like this: imagine you're baking a cake. Getting the final cake right is great, but did you follow the recipe correctly? Did you measure the ingredients accurately? Did you mix them in the right order? The deductive consistency metric is like checking all those steps in the AI's reasoning "recipe".

Essentially, deductive reasoning boils down to two key things:

• Understanding the rules. Can the AI correctly grasp the information given in the problem? It's like understanding the cake recipe's list of ingredients and their amounts.
• Inferring the next steps. Can the AI logically deduce what steps to take based on those rules? Like knowing to cream the butter and sugar before adding the eggs.

The researchers wanted to know where the AIs were going wrong. Were they misunderstanding the problem setup? Or were they messing up the logical steps needed to reach the solution?

Now, here’s where it gets really clever. The researchers realized that existing math problem sets might have been... well, memorized by the AIs. So, they created novel problems, slightly altered versions of existing ones. Think of it as tweaking the cake recipe just a little bit – maybe substituting one type of flour for another – to see if the AI can still bake a delicious "cake" of a solution.

They used the GSM-8k dataset, which is basically a collection of grade school math problems. What they found was really interesting:

• AIs are pretty good at handling lots of information. Even when they added more and more facts to the problem, the AIs didn't get too confused. It's like being able to handle a cake recipe with tons of different ingredients.
• But... the AIs struggled when they had to take multiple logical steps. This is where things fell apart. Imagine having to not just follow the recipe, but also invent new steps based on the initial instructions!

"Prediction over multiple hops still remains the major source of error compared to understanding input premises."

This is a huge deal, because it suggests that these AIs aren't truly "reasoning" in the way we might think. They're good at processing information, but not so good at stringing together a long chain of logical deductions.

So, why does this research matter?

• For AI developers: It points to a specific area where AIs need improvement: multi-step reasoning. We need to build models that can not just understand information, but also make longer, more complex deductions.
• For educators: It highlights the importance of teaching reasoning skills, not just memorization. We need to equip students with the ability to solve problems they've never seen before.
• For everyone: As AI becomes more integrated into our lives, understanding its limitations is crucial. We need to be aware of when an AI can be trusted and when it might be making mistakes due to flawed reasoning.

This research frames AI reasoning as a sort of "window" of input and reasoning steps. It's like the AI can only see a certain distance ahead in the problem-solving process.

Now, this all leads to a few interesting questions to ponder:

• If AI struggles with multi-step reasoning, what does that say about its ability to handle really complex, real-world problems that require many interconnected deductions?
• Could we design new training methods that specifically focus on improving an AI's ability to "see" further ahead in the reasoning process?
• How do we balance the impressive performance of AI on some tasks with its limitations in areas like deductive reasoning?

That's the scoop on this paper, learning crew! Hopefully, this gives you a better understanding of the challenges and opportunities in the world of AI reasoning. Until next time, keep those brains buzzing!

Credit to Paper authors: Atharva Pandey, Kshitij Dubey, Rahul Sharma, Amit Sharma