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Shortcut “Reasoning”

Language models only “reason” through shortcuts

They only solve problems through statistical correlation. They effect pseudo-reasoning by replaying and combining records of prior reasoning.

Shortcut “reasoning” depends on the training data

Because their pseudo-reasoning is based on correlations, the simplest correlation will always win out. For example, an AI trained to detect COVID-19 from chest x-rays ended up only detecting the position of the patient. Prone patients were sicker and so more likely to have COVID-19.

Shortcut “reasoning” is extremely fragile

Correlative pseudo-reasoning breaks very easily. Language model reasoning often falls apart when the question is rephrased or reworded, which is the unpredictability that gives users the impression that prompts work more like magic incantations than commands.

“Reasoning” performance is due to data contamination

The effectiveness of language models at completing standardised tests, for example, seems to be entirely down to those exams, practice questions, and documentation on them being included in the training data set, making the pseudo-reasoning correlations very simple.

AI models can’t handle the genuinely new

It’s “reasoning” mechanism is entirely based on finding patterns in existing data. It will not be able to handle genuinely new problems or circumstances.

AI “reasoning” is extremely vulnerable to simple attacks

AI inability to handle novel problems makes them extremely vulnerable. An attacker can manipulate or bypass a model’s reasoning simply by employing unusual, even ridiculous, tactics.


Cover for the book 'The Intelligence Illusion'

These cards were made by Baldur Bjarnason.

They are based on the research done for the book The Intelligence Illusion: a practical guide to the business risks of Generative AI .

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