Abstract: Provided herein are systems and methods that can detect errors and hallucinations in output data produced by generative models. The detection systems described herein may compare information from the generative model output data with ground truth information to determine whether the generative model output data comprises errors and/or hallucinations. The systems and methods described herein may generate an output indicative of whether the generative model output data comprises errors and/or hallucinations. The systems and methods described herein can readily detect false information generated by NLG systems, thus potential harms of reliance on false information can be minimized.

Abstract: Provided herein are systems and methods that can detect errors and hallucinations in output data produced by generative models. The detection systems described herein may compare information from the generative model output data with ground truth information to determine whether the generative model output data comprises errors and/or hallucinations. The systems and methods described herein may generate an output indicative of whether the generative model output data comprises errors and/or hallucinations. The systems and methods described herein can readily detect false information generated by NLG systems, thus potential harms of reliance on false information can be minimized.