Back in 2019, a group of computer scientists performed a now-famous experiment with far-reaching consequences for artificial intelligence research. At the time, machine vision algorithms were becoming capable of recognizing a wide range of objects with some recording spectacular results in the standard tests used to assess their abilities.
But there was a problem with the method behind all these tests. Almost all the algorithms were trained on a database of labelled images, known as ImageNet. The database contained millions of images which had been carefully described in human-written text to help the machines learn. This effort was crucial for the development of machine vision and ImageNet became a kind of industry standard.
In this way, the computer scientists used a subset of the images to train algorithms to identify a strawberry, a table, a human face and so on, using labelled images in the dataset. They then used a different subset of images to test the algorithms. Over time, computer scientists claimed that their algorithms were becoming increasingly good at recognizing objects in the real world.
Image Recognition
But privately, researchers began to wonder whether this was really true. Because the ImageNet database was becoming so famous, an alternative explanation was that its images, or ones very like them, were leaking into the real world. So AI systems trained on them were just recognizing images they had already seen.
At the time, there was no way to test this because there were no high-quality image databases that hadn’t already been used to train the algorithms.
All that changed when a team from the University of California, Berkeley, created a new dataset of carefully labelled images that they knew the algorithms could not have seen. They then asked the algorithms to identify the objects in the images and found they weren’t as good as everyone had claimed.
Their experiment became a famous example of the pitfalls of relying on single databases for testing machines. Without careful management of this database, AI systems can seem to be good at a task in general but are really only repeating what they have already learnt.
That brings us to the current generation of AI systems which are good at solving certain types of mathematics problems written out in words. For example, “James writes a 3-page letter to 2 different friends twice a week. How many pages does he write a year?”.
The fact that AI systems can answer questions like this suggests they are able to reason. In fact, there is a special database called GSM8K that computer scientists use to test AI system’s reasoning ability. This question is taken from there.
GSM8K is a “dataset of 8.5K high quality linguistically diverse grade school math word problems created by human problem writers.” It consists of some 7500 questions for training an AI system and 1000 questions to test the system.
Over the years, AI systems have become increasingly better at answering these questions. That has led to various claims that AI systems are becoming better at the kind of reasoning needed to solve these problems.
But there is another possibility. This is that GSM8K has become so well known that the test questions have begun to leak into the wild. As a result, AI systems may come across them during their broader benchmark training. So rather than answering them by reasoning, they could just be repeating the answer they saw during their training.
“There is growing concern that some of this performance actually reflects dataset contamination, where data closely resembling benchmark questions leaks into the training data, instead of true reasoning ability,” say Hugh Zhang and colleagues at Scale AI, a start-up based in San Francisco focused on cleaning data for use by AI systems.
Following the lead by the Berkeley researchers, the Scale AI team decided to test this idea by developing their own mathematics test of 1250 questions. They call this GSM1k and have carefully ensured that it closely resembles the GSM8K test but has never been published.
“We took extensive efforts to ensure that GSM1k had a similar distribution of difficulty to GSM8k to ensure an apples-to-apples comparison,” they say. “We ensure that the two benchmarks are comparable across important metrics such as human solve rates, number of steps in solution, answer magnitude, and more.”
They then tested a wide range of AI systems on the GSM1k problems to see how well they performed. And the results make for interesting reading.
It turns out that a large number of AI systems perform significantly worse on the new data set than on the original. “When evaluating leading open- and closed-source LLMs on GSM1k, we observe accuracy drops of up to 13 per cent,” say Zhang and co.
The team point to several systems that seem particularly vulnerable, such as the French AI system Mistral and Microsoft’s smaller AI system, Phi.
Reasoned Response
However, others show little or no drop in performance. These include ChatGPT, Claude and Gemini. Zhang and co say that these models might be better at mathematical reasoning or that their model builders are more careful about data contamination.
The team also ask these systems to generate questions from GSM8K. It turns out that their ability to do this is closely correlated with the difference in their ability to answer GSM1k and GSM8k questions. This strongly suggests the models have partially memorized examples from GSM8k, say Zhang and co.
It’s not all bad news, however, “Many models, even the most heavily overfit families, show strong signs of generalizable mathematical reasoning,” they conclude.
That’s interesting work that reveals the limitations of the benchmarking processes used to test the ability of AI systems. Even though these tests show that there has been significant progress in the reasoning ability of AI systems in recent years, caution is needed in interpreting progress.
The bigger question is how more advanced AI systems can be benchmarked accurately, particularly when the datasets are so difficult to curate and as their abilities become superhuman. It raises the very real possibility that at some point in the future, we will never know the true capability of these machines.
Ref: A Careful Examination of Large Language Model Performance on Grade School Arithmetic : arxiv.org/abs/2405.00332
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