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Mike Young
Mike Young

Posted on • Originally published at aimodels.fyi

New Test Reveals How AI Models Hallucinate When Given Distorted Inputs

This is a Plain English Papers summary of a research paper called New Test Reveals How AI Models Hallucinate When Given Distorted Inputs. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.

Overview

  • This paper proposes a new benchmark, called Hallu-PI, for evaluating hallucination in multi-modal large language models (MM-LLMs) when given perturbed inputs.
  • Hallucination refers to the generation of irrelevant or factually incorrect content by language models.
  • The authors test several state-of-the-art MM-LLMs on Hallu-PI and provide insights into their hallucination behaviors.

Plain English Explanation

The researchers created a new way to test how well multi-modal large language models (MM-LLMs) handle hallucination. Hallucination is when language models generate informat...

Click here to read the full summary of this paper

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