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LLMs excel at inductive reasoning but struggle with deductive tasks, new research shows
LLMs are known for reasoning powers. But new research from MIT and UCLA shows LLMs differ widely between inductive and deductive reasoning.
SolverLearner first prompts the LLM to generate a function that maps input data points to their corresponding output values based solely on a set of input-output examples. The results showed that both LLMs consistently exhibited remarkable inductive reasoning capabilities, achieving near-perfect accuracy on tasks that required them to learn from examples and infer the underlying mapping function. This study is a sobering reminder that we have yet a lot to learn about the abilities of these black boxes that are becoming part of a growing number of applications.
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