Executive summary: Benchmark performance is an unreliable measure of general AI reasoning capabilities due to overfitting, poor real-world relevance, and lack of generalisability, as demonstrated by adversarial testing and interpretability research.
Key points:
Benchmarks encourage overfitting—LLMs often train on benchmark data, leading to inflated scores without true capability improvements (a case of Goodhart’s law).
Limited real-world relevance—Benchmarks rarely justify why their tasks measure intelligence, and many suffer from data contamination and quality control issues.
LLMs struggle with generalisation—Studies show they rely on statistical shortcuts rather than learning underlying problem structures, making them sensitive to minor prompt variations.
Adversarial testing exposes flaws—LLMs fail tasks that require true reasoning, such as handling irrelevant information or understanding problem structure beyond superficial cues.
“Reasoning models” are not a breakthrough—New models like OpenAI’s o3 use heuristics and reinforcement learning but still lack genuine generalisation abilities.
Benchmark reliance leads to exaggerated claims—Improved scores do not equate to real cognitive progress, highlighting the need for more rigorous evaluation methods.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.
Executive summary: Benchmark performance is an unreliable measure of general AI reasoning capabilities due to overfitting, poor real-world relevance, and lack of generalisability, as demonstrated by adversarial testing and interpretability research.
Key points:
Benchmarks encourage overfitting—LLMs often train on benchmark data, leading to inflated scores without true capability improvements (a case of Goodhart’s law).
Limited real-world relevance—Benchmarks rarely justify why their tasks measure intelligence, and many suffer from data contamination and quality control issues.
LLMs struggle with generalisation—Studies show they rely on statistical shortcuts rather than learning underlying problem structures, making them sensitive to minor prompt variations.
Adversarial testing exposes flaws—LLMs fail tasks that require true reasoning, such as handling irrelevant information or understanding problem structure beyond superficial cues.
“Reasoning models” are not a breakthrough—New models like OpenAI’s o3 use heuristics and reinforcement learning but still lack genuine generalisation abilities.
Benchmark reliance leads to exaggerated claims—Improved scores do not equate to real cognitive progress, highlighting the need for more rigorous evaluation methods.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.