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AssigneeServiceNow, Inc.
InventorsSreenija Kantamneni, Monica Chowdary Chimmiri, Praison Selvaraj, Bharadwaj Mahidhar Gopal Yadati, Srinivasa Rao Vundavalli, Ranga Prasad Chenna
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Methods are provided for generation of natural-language explanations for machine learning model outputs that exhibit reduced hallucinations and higher accuracy while incurring lower computational costs and enhancing privacy. These methods include determining, for the textual and categorical features of an input, respective importance values regarding the degree to which the overall model output was influenced by the input features. These values, along with the input features themselves and the overall model output, are then applied to a natural language model to generate an explanation of the overall model output with respect to the input features. The importance values can be used to prune input features that were least important to the overall model output, further reducing hallucinations, increasing accuracy, and reducing overall computational cost by allowing models with smaller input widths or otherwise less computationally expensive natural language models to be used.
A method comprising:
A non-transitory computer-readable medium, having stored thereon program instructions that, upon execution by a computing system, cause the computing system to perform operations comprising:
A system comprising:
Dependencies are inferred from the claim text.