Amazon Technologies, Inc.

Hyungseo Park

Chenlei Guo

Xing Fan

Narendra Gyanchandani

Robert O'Neal Copeland

Devices and techniques are generally described for arbitration between LLM-based and intent-based natural language processing flows. In various examples, an automatic speech recognition (ASR) component may generate first ASR output data representing the first natural language input. A first machine learning model may select an LLM-based natural language processing flow. The first machine learning model may be trained to select between at least the LLM-based and non-LLM-based natural language processing flows. The first ASR output data may be processed using the LLM-based processing flow. The LLM-based processing flow may generate first executable data.

Language model arbitration for natural language processing

Techniques for determining one or more responses associated with one or more components that are responsive to a user input are described. The system receives a user input and causes one or more components to generate one or more responses associated with the user input. The system determines one or more of the responses are responsive to the user input, causes one or more actions associated with the responses to be performed, and outputs a natural language summary of the one or more responses. If the system determines that none of the responses are responsive to the user input and/or an ambiguity exists with respect to the user input, the system can generate a request for additional information usable to resolve the ambiguity, which may be sent to another component of the system and/or output to the user that provided the user input.

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Seattle, WA, United States of America

Hyungseo Park