Computation and Language - What the HellaSwag? On the Validity of Common-Sense Reasoning Benchmarks

Alright learning crew, welcome back to PaperLedge! Ernis here, ready to dive into some research that's got me thinking about how we test AI. Today, we're tackling a paper that throws a wrench into how we measure something called common-sense reasoning in language models.

Now, what is common-sense reasoning for an AI? Think of it like this: it's not just knowing facts, like "the sky is blue." It's understanding why the sky is usually blue, knowing that if you drop something, it'll fall, and generally being able to navigate the world like a reasonably intelligent human. It's the kind of knowledge you just know, without having to be explicitly taught.

To test this in AI, researchers use things called benchmarks – basically, standardized tests. One really popular one is called HellaSwag. The idea behind HellaSwag is to give the AI a situation and see if it can predict what happens next in a plausible, common-sense way.

Here’s where things get interesting. This paper we're looking at argues that HellaSwag isn't actually measuring common sense very well. The authors claim it has some serious problems that make the results unreliable. Think of it like this: imagine trying to measure someone's musical ability with a test that's full of typos, uses confusing instructions, and sometimes has more than one right answer! You wouldn't get a very accurate picture, would you?

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So, what are these problems with HellaSwag? The paper highlights a few:

• Grammar Gone Wild: Apparently, HellaSwag has basic grammatical errors and typos. If the test itself is flawed, how can we trust the results?
• Misleading Prompts: Some of the questions are just confusing or set up in a way that leads to incorrect answers, even if the AI does have common sense.
• Multiple Right Answers: Sometimes, the test offers several options that could all be considered correct. This makes it difficult to determine if the AI is truly understanding the situation or just guessing.

“...if models are evaluated only on answer texts, or with "Lorem ipsum dolor..." instead of the question, more than 65% of model predictions remain the same...”

But here's the kicker: the authors even showed that if they replaced the actual questions with gibberish (like "Lorem ipsum"), the AI still gave the same answers most of the time! That suggests the AI isn't actually reading the question and using common sense at all. It's finding patterns elsewhere -- maybe in the way the answers are phrased.

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Why does this matter? Well, these benchmarks are used to decide which AI models are "better" than others. Companies and researchers use these scores to choose which models to use in real-world applications. If the benchmarks are flawed, we could be making bad decisions and choosing AI that seems smart but isn't really reasoning effectively.

The authors conclude that HellaSwag, in its current form, shouldn't be used for evaluating common-sense reasoning. They even created a cleaned-up version called GoldenSwag, which they believe is a much better way to test these capabilities. They also provide suggestions to make future benchmarks better.

So, what does this mean for us?

• For AI Researchers: This paper is a wake-up call to be more critical of the benchmarks we use. We need to make sure we're actually measuring what we think we're measuring.
• For Businesses Using AI: Don't just blindly trust benchmark scores. Understand the limitations of these tests and consider other ways to evaluate AI before making important decisions.
• For Everyone Else: This highlights that AI, while impressive, is still under development. We need to be aware of its limitations and not assume it's always making decisions based on common sense.

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This research leaves me with a few questions for us to chew on:

• If current benchmarks aren't accurately measuring common sense, how should we be testing AI's reasoning abilities? What would a truly valid common-sense reasoning test look like?
• The authors created GoldenSwag, but what are the limits of just "cleaning up" an existing benchmark? Do we ultimately need to start from scratch to create more robust tests?
• Given that so many AI applications rely on these potentially flawed benchmarks, how much are we overestimating the true capabilities of current AI systems?

That's all for this episode of PaperLedge! Let me know what you think of this research in the comments. Until next time, keep learning, crew!

Credit to Paper authors: Pavel Chizhov, Mattia Nee, Pierre-Carl Langlais, Ivan P. Yamshchikov