Abstract: In some aspects, the techniques described herein relate to a method including: providing, to a parallel model training platform, a plurality of domain datasets; training, by the parallel model training platform, a plurality of generalist models in parallel, wherein each generalist model of the plurality of generalist models is trained in parallel using a corresponding one of the plurality of domain datasets, and wherein training the plurality of generalist models in parallel generates a corresponding expert model for each generalist model in the plurality of generalist models; executing, by the parallel model training platform, a model parameter averaging process, wherein the model parameter averaging process take each corresponding expert model as input; and generating, by the parallel model training platform and as output of the model parameter averaging process, an average-of-domain-experts (AoDE) model.

Abstract: Various methods, apparatuses/systems, and media for automatic real-time identification of dissatisfaction data are disclosed. A receiver receives real-time stream of call transcript data generated during a call or a chat between a customer and an agent. A processor implements a machine learning model that includes predefined complaint data; applies the received call transcript data onto the machine learning model; compares, in response to applying, the call transcript data with predefined complaint data; generates a first similarity score, based on comparing, that identifies how similar the call transcript data is compared to the predefined complaint data; and automatically identifies the call transcript data as a first dissatisfaction data based on determining that the first similarity score is equal to or more than a predetermined threshold value.

Abstract: Aspects of the subject disclosure may include, for example, obtaining a first set of tags and a first set of descriptions associated with a set of content items, causing an LLM to generate a plurality of tags based on the first set of tags and the first set of descriptions, resulting in a second set of tags, wherein the second set of tags includes the first set of tags and the plurality of tags, and causing the LLM to apply one or more tags from the second set of tags to one or more content items based on information regarding the one or more content items. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, obtaining a model, obtaining input audio, processing the input audio to generate a transcript, obtaining at least one summary based on the transcript, identifying at least one error or inconsistency in the transcript or the at least one summary based on the model, and implementing, based on the identifying, a correction or a clarification in respect of the at least one error or inconsistency. Other aspects are disclosed.

Abstract: In some aspects, the techniques described herein relate to a method including: providing, to a parallel model training platform, a plurality of domain datasets; training, by the parallel model training platform, a plurality of generalist models in parallel, wherein each generalist model of the plurality of generalist models is trained in parallel using a corresponding one of the plurality of domain datasets, and wherein training the plurality of generalist models in parallel generates a corresponding expert model for each generalist model in the plurality of generalist models; executing, by the parallel model training platform, a model parameter averaging process, wherein the model parameter averaging process take each corresponding expert model as input; and generating, by the parallel model training platform and as output of the model parameter averaging process, an average-of-domain-experts (AoDE) model.

Abstract: Various methods, apparatuses/systems, and media for automatic real-time identification of dissatisfaction data are disclosed. A receiver receives real-time stream of call transcript data generated during a call or a chat between a customer and an agent. A processor implements a machine learning model that includes predefined complaint data; applies the received call transcript data onto the machine learning model; compares, in response to applying, the call transcript data with predefined complaint data; generates a first similarity score, based on comparing, that identifies how similar the call transcript data is compared to the predefined complaint data; and automatically identifies the call transcript data as a first dissatisfaction data based on determining that the first similarity score is equal to or more than a predetermined threshold value.