Abstract: There is provided a method of improving the operation of a generative AI large language model (LLM)-based data processing system, by operating the LLM-based system in conjunction with a non-LLM data processing system; and in which (a) the LLM-based system sends a continuation as an input to the non-LLM system, and (b) the non-LLM system (i) uses symbolic representations to perform non-statistical reasoning on the input from the LLM-based system and (ii) generates a reasoned prompt or other context.

Abstract: There is provided a method of improving the operation of a generative AI large language model (LLM)-based data processing system, by operating the LLM-based system in conjunction with a non-LLM data processing system; and in which (a) the LLM-based system sends a continuation as an input to the non-LLM system, and (b) the non-LLM system (i) uses symbolic representations to perform non-statistical reasoning on the input from the LLM-based system and (ii) generates a reasoned prompt or other context.

Abstract: There is provided a method of improving the operation of a generative AI large language model (LLM)-based data processing system, by operating the LLM-based system in conjunction with a non-LLM data processing system; and in which (a) the LLM-based system sends a continuation as an input to the non-LLM system, and (b) the non-LLM system (i) uses symbolic representations to perform non-statistical reasoning on the input from the LLM-based system and (ii) generates a reasoned prompt or other context.

Abstract: There is provided a method of improving the operation of a generative AI large language model (LLM)-based data processing system, by operating the LLM-based system in conjunction with a non-LLM data processing system; and in which (a) the LLM-based system sends a continuation as an input to the non-LLM system, and (b) the non-LLM system (i) uses symbolic representations to perform non-statistical reasoning on the input from the LLM-based system and (ii) generates a reasoned prompt or other context.

Abstract: There is provided a method of improving the operation of a generative AI large language model (LLM)-based data processing system, by operating the LLM-based system in conjunction with a non-LLM data processing system; and in which (a) the LLM-based system sends a continuation as an input to the non-LLM system, and (b) the non-LLM system (i) uses symbolic representations to perform non-statistical reasoning on the input from the LLM-based system and (ii) generates a reasoned prompt or other context.

Abstract: There is provided a method of improving the operation of a generative AI large language model (LLM)-based data processing system, by operating the LLM-based system in conjunction with a non-LLM data processing system; and in which (a) the LLM-based system sends a continuation as an input to the non-LLM system, and (b) the non-LLM system (i) uses symbolic representations to perform non-statistical reasoning on the input from the LLM-based system and (ii) generates a reasoned prompt or other context.

Abstract: There is provided a method of improving the operation of a generative AI large language model (LLM)-based data processing system, by operating the LLM-based system in conjunction with a non-LLM data processing system; and in which (a) the LLM-based system sends a continuation as an input to the non-LLM system, and (b) the non-LLM system (i) uses symbolic representations to perform non-statistical reasoning on the input from the LLM-based system and (ii) generates a reasoned prompt or other context.

Abstract: There is provided a method of improving the operation of a generative AI large language model (LLM)-based data processing system, by operating the LLM-based system in conjunction with a non-LLM data processing system; and in which (a) the LLM-based system sends a continuation as an input to the non-LLM system, and (b) the non-LLM system (i) uses symbolic representations to perform non-statistical reasoning on the input from the LLM-based system and (ii) generates a reasoned prompt or other context.

Abstract: There is provided a method of improving the operation of a generative AI large language model (LLM)-based data processing system, by operating the LLM-based system in conjunction with a non-LLM data processing system; and in which (a) the LLM-based system sends a continuation as an input to the non-LLM system, and (b) the non-LLM system (i) uses symbolic representations to perform non-statistical reasoning on the input from the LLM-based system and (ii) generates a reasoned prompt or other context.

Abstract: There is provided a method of improving the operation of a generative AI large language model (LLM)-based data processing system, by operating the LLM-based system in conjunction with a non-LLM data processing system; and in which (a) the LLM-based system sends a continuation as an input to the non-LLM system, and (b) the non-LLM system (i) uses symbolic representations to perform non-statistical reasoning on the input from the LLM-based system and (ii) generates a reasoned prompt or other context.

Abstract: There is provided a computer implemented method in which a deep learning model detects and interprets real time events from an input data stream, in which the detected and interpreted events are output in a structured, machine-readable representation of data that conforms to a machine-readable language.

Abstract: There is provided a method of improving the operation of a generative AI large language model (LLM)-based data processing system, by operating the LLM-based system in conjunction with a non-LLM data processing system; and in which (a) the LLM-based system sends a continuation as an input to the non-LLM system, and (b) the non-LLM system (i) uses symbolic representations to perform non-statistical reasoning on the input from the LLM-based system and (ii) generates a reasoned prompt or other context.

Abstract: There is provided a method of improving the operation of a generative AI large language model (LLM)-based data processing system, by operating the LLM-based system in conjunction with a non-LLM data processing system; in which the LLM-based system sends a continuation as an input to the non-LLM system; and in which the non-LLM system (a) uses symbolic representations to (i) generate factual assertions and/or (ii) generate non-statistical reasoning steps, in each case by processing the input sent from the LLM-based system and (b) stores the factual assertions and/or non-statistical reasoning steps (“stored facts and reasoning data”) in a memory for long term re-use by the LLM-based system and/or the non-LLM system.

Abstract: There is provided a computer-implemented method for ensuring that a large language model (LLM) generates original text, including (i) providing or accessing a database of previous text that the LLM should not generate, wherein the database includes text used to train the LLM; (ii) checking potential continuations generated by the LLM against the database; (iii) when a potential continuation generated by the LLM matches text in the database, adjusting the potential continuation generated by the LLM to no longer match that text in the database, to produce an adjusted potential continuation, and (iv) storing the adjusted potential continuation.

Abstract: There is provided a method of improving the operation of a generative AI large language model (LLM)-based data processing system, by operating the LLM-based system in conjunction with a non-LLM data processing system; in which (a) the LLM-based system sends a continuation as an input to the non-LLM system; and in which the non-LLM system (i) uses symbolic representations to generate non-statistical reasoning steps by processing the input from the LLM-based system and (ii) provides the reasoning steps to the LLM-based system to enable the LLM-based system to include visible reasoning steps in a revised continuation or other output.

Abstract: There is provided a computer-implemented method for ensuring that a large language model (LLM) generates original text, including (i) providing or accessing a database of previous text that the LLM should not generate, wherein the database includes text used to train the LLM; (ii) checking potential continuations generated by the LLM against the database; (iii) when a potential continuation generated by the LLM matches text in the database, adjusting the potential continuation generated by the LLM to no longer match that text in the database, to produce an adjusted potential continuation, and (iv) storing the adjusted potential continuation.

Abstract: There is provided a computer-implemented method for ensuring that a large language model (LLM) generates original text, including (i) providing or accessing a database of previous text that the LLM should not generate, wherein the database includes text used to train the LLM; (ii) checking potential continuations generated by the LLM against the database; (iii) when a potential continuation generated by the LLM matches text in the database, adjusting the potential continuation generated by the LLM to no longer match that text in the database, to produce an adjusted potential continuation, and (iv) storing the adjusted potential continuation.

Abstract: There is provided a computer-implemented method for ensuring that a large language model (LLM) generates original text, including (i) providing or accessing a database of previous text that the LLM should not generate, wherein the database includes text used to train the LLM; (ii) checking potential continuations generated by the LLM against the database; (iii) when a potential continuation generated by the LLM matches text in the database, adjusting the potential continuation generated by the LLM to no longer match that text in the database, to produce an adjusted potential continuation, and (iv) storing the adjusted potential continuation.

Abstract: There is provided a computer-implemented method for ensuring that a large language model (LLM) generates original text, including (i) providing or accessing a database of previous text that the LLM should not generate, wherein the database includes text used to train the LLM; (ii) checking potential continuations generated by the LLM against the database; (iii) when a potential continuation generated by the LLM matches text in the database, adjusting the potential continuation generated by the LLM to no longer match that text in the database, to produce an adjusted potential continuation, and (iv) storing the adjusted potential continuation.

Abstract: There is provided a computer-implemented method for ensuring that a large language model (LLM) generates original text, including (i) providing or accessing a database of previous text that the LLM should not generate, wherein the database includes text used to train the LLM; (ii) checking potential continuations generated by the LLM against the database; (iii) when a potential continuation generated by the LLM matches text in the database, adjusting the potential continuation generated by the LLM to no longer match that text in the database, to produce an adjusted potential continuation, and (iv) storing the adjusted potential continuation.