US 12,444,412 B2
Large language model (LLM)-based correction based on a multi-tool prompt
Deepak Babu Rajaram Piskala, Bellevue, WA (US)
Assigned to Amazon Technologies, Inc., Seattle, WA (US)
Filed by Amazon Technologies, Inc., Seattle, WA (US)
Filed on Sep. 15, 2023, as Appl. No. 18/369,049.
Prior Publication US 2025/0095641 A1, Mar. 20, 2025
Int. Cl. G10L 15/183 (2013.01)
CPC G10L 15/183 (2013.01) 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving, via a user interface, user input including user-provided information and indicating a request for a task to be performed on the user-provided information, the task comprising text correction, the user-provided information comprising text that is output by an automatic speech recognition (ASR) process and that includes a first entity identifier;
generating, by using a large language model associated with a prompt, a first input to a first tool based at least in part on the user input, the prompt indicating a sequence of steps to perform for the task, a plurality of tools available to the large language model, and an execution format for each step, the first tool corresponding to a first step of the sequence of steps, the first input being based at least in part on a first execution format for the first step;
determining, by using the large language model, a first output of the first tool in response to the first input;
determining, by using the large language model, that a second step of the sequence is to be performed based at least in part on the first output;
generating, by using the large language model, a second input to a second tool based at least in part on the first output, the second tool corresponding to the second step, the second input being based at least in part on a second execution format for the second step;
determining, by using the large language model, a second output of the second tool in response to the second input;
determining, by using the large language model, an update to the user-provided information based at least in part on the second output and a completion of the task, the update comprising a correction to the first entity identifier; and
causing the user interface to present the update.