RESPONDING TO HALLUCINATIONS IN GENERATIVE LARGE LANGUAGE MODELS
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United States of America
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Mar 20, 2025
app pub date -
May 30, 2024
filing date -
May 30, 2024
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Abstract
Techniques for correcting hallucinations produced by generative large language models (LLMs). In one technique, a computing system accesses first output generated by an LLM. The computing system identifies, within the first output, a plurality of assertions. The computing system determines that a first assertion in the plurality of assertions is false. The computing system generates a prompt that indicates that the first assertion is false. The computing system submits the prompt as input to the LLM. The computing system accesses second output that is generated by the LLM, where the second output includes a second assertion that is different than the first assertion and corresponds to the first assertion.
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