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Can AI Think Its Own Thoughts? Learning to Question Inputs in LLMs
LLMs can generate code amazingly fast — but what happens when the input premise is wrong?
In this episode of Decode: Science, we explore “Refining Critical Thinking in LLM Code Generation: A Faulty Premise–based Evaluation Framework” (FPBench). Jialin Li and colleagues designed an evaluation system that tests how well 15 popular models recognize and handle faulty or missing premises, revealing alarming gaps in their reasoning abilities. We decode what FPBench is, why it matters for AI trust, and what it could take to make code generation smarter.
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By Plain ScienceLLMs can generate code amazingly fast — but what happens when the input premise is wrong?
In this episode of Decode: Science, we explore “Refining Critical Thinking in LLM Code Generation: A Faulty Premise–based Evaluation Framework” (FPBench). Jialin Li and colleagues designed an evaluation system that tests how well 15 popular models recognize and handle faulty or missing premises, revealing alarming gaps in their reasoning abilities. We decode what FPBench is, why it matters for AI trust, and what it could take to make code generation smarter.