Updating population language models based on changes made by user clusters

A non-transitory computer-readable memory containing instructions that, when executed by a computing system, implement a method of selectively updating local language models used to predictively complete user input, the method comprising:

• receiving information about local language models including a first local language model and other local language models, wherein the information about local language models includes information about local language model events representing user changes to at least one of the local language models;
• receiving information characterizing users associated with the user changes;
• identifying a user cluster from the received characterization information;
• identifying a set of the local language models including the first local language model and one or more other local language models, based at least in part on the other local language models having local language model events similar to first local language model events;
• for each local language model in the set, identifying additional local language model events;
• identifying words associated with the additional local language model events;
• identifying other words that are connected to the associated words;
• generating modifications to the first local language model using the additional local language model event information of one or more of the identified other local language models in the set, and also using the user cluster, the associated words, and the other words;
• filtering the generated modifications by excluding events associated with a blacklist of vocabulary not to be added or events associated with a whitelist of vocabulary not to be deleted before updating the first local language model with the generated modifications;
• and
• updating the first local language model with the generated modifications based on providing the generated modifications to a computing device of one or more users in the user cluster.