Methods and systems for detecting disinformation generated by large language models
AssigneeArizona Board of Regents on behalf of Arizona State University
InventorsBohan Jiang, Zhen Tan, Ayushi Nirmal, Huan Liu
Life of this patent
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Abstract
Systems and methods for detecting disinformation generated from large learning models (LLMs) are disclosed including implementation of one or more prompts that guide an artificial intelligence (AI) model detecting such disinformation. More specifically, prompting techniques can be used to generate new training datasets for the AI model, and other prompting techniques can be implemented to train the AI model on the new training datasets.
Claims20
A computer-implemented method for detecting disinformation generated by a large language model (LLM), comprising:
- accessing a dataset of human-written news articles designated as true or fake;
- generating, using at least one LLM, one or more disinformation datasets based on the dataset of human-written news articles, comprising:
- applying at least two distinct prompting techniques including a chain-of-thought prompting technique configured to emulate human cognitive processes, wherein the one or more disinformation datasets are used to develop a structured prompt template;
- guiding the LLM with the structured prompt template to analyze an input article, the structured prompt template comprising instructions that direct the LLM to extract contextual elements from the input article, reformulate the contextual elements into a logical narrative, and evaluate the narrative for consistency with known facts, and to output a prediction of whether the input article comprises disinformation.
A system for detecting disinformation generated by a large language model (LLM), the system comprising:
- at least one processor;
- and
- at least one memory storing instructions that, when executed by the at least one processor, cause the system to:
- access a dataset of human-written news articles classified as true or fake;
- generate, using at least one LLM, one or more disinformation datasets based on the dataset of human-written news articles, the generating comprising applying at least two distinct prompting techniques including a chain-of-thought prompting technique configured to emulate human cognitive processes, wherein the one or more disinformation datasets are used to configure a structured prompt template;
- and
- guide the LLM with the structured prompt template to analyze an input article, the structured prompt template comprising instructions that direct the LLM to extract contextual elements from the input article, reformulate the contextual elements into a logical narrative, evaluate the narrative for consistency with known facts, and output a prediction of whether the input article comprises disinformation.
Dependencies are inferred from the claim text.