You say “Not even close.” in response to the suggestion that Apple’s research can be used to improve benchmarks for AI performance, but then later say the article talks about how we might need different approaches to achieve reasoning.
Now, mind you - achieving reasoning can only happen if the model is accurate and works well. And to have a good model, you must have good benchmarks.
Not to belabor the point, but here’s what the article and study says:
The article talks at length about the reliance on a standardized set of questions - GSM8K, and how the questions themselves may have made their way into the training data. It notes that modifying the questions dynamically leads to decreases in performance of the tested models, even if the complexity of the problem to be solved has not gone up.
The third sentence of the paper (Abstract section) says this “While the
performance of LLMs on GSM8K has significantly improved in recent years, it remains unclear
whether their mathematical reasoning capabilities have genuinely advanced, raising questions
about the reliability of the reported metrics.” The rest of the abstract goes on to discuss (paraphrased in layman’s terms) that LLM’s are ‘studying for the test’ and not generally achieving real reasoning capabilities.
By presenting their methodology - dynamically changing the evaluation criteria to reduce data pollution and require models be capable of eliminating red herrings - the Apple researchers are offering a possible way benchmarking can be improved.
Which is what the person you replied to stated.
Adding the benchmark back into the training process doesn’t mean you get an LLM the can weed out irrelevant data, what you get is an LLM that can pass the new metric and you have to design a new metric with different semantic patterns to actually know if it’s “eliminating red herrings”.
You say “Not even close.” in response to the suggestion that Apple’s research can be used to improve benchmarks for AI performance, but then later say the article talks about how we might need different approaches to achieve reasoning.
Now, mind you - achieving reasoning can only happen if the model is accurate and works well. And to have a good model, you must have good benchmarks.
Not to belabor the point, but here’s what the article and study says:
The article talks at length about the reliance on a standardized set of questions - GSM8K, and how the questions themselves may have made their way into the training data. It notes that modifying the questions dynamically leads to decreases in performance of the tested models, even if the complexity of the problem to be solved has not gone up.
The third sentence of the paper (Abstract section) says this “While the performance of LLMs on GSM8K has significantly improved in recent years, it remains unclear whether their mathematical reasoning capabilities have genuinely advanced, raising questions about the reliability of the reported metrics.” The rest of the abstract goes on to discuss (paraphrased in layman’s terms) that LLM’s are ‘studying for the test’ and not generally achieving real reasoning capabilities.
By presenting their methodology - dynamically changing the evaluation criteria to reduce data pollution and require models be capable of eliminating red herrings - the Apple researchers are offering a possible way benchmarking can be improved.
Which is what the person you replied to stated.
The commenter is fairly close, it seems.
Adding the benchmark back into the training process doesn’t mean you get an LLM the can weed out irrelevant data, what you get is an LLM that can pass the new metric and you have to design a new metric with different semantic patterns to actually know if it’s “eliminating red herrings”.