Abstract: A model training service of a provider network may receive content items as training data. For example, the content items may be documents with certain portions labeled as stale. The model training service may train one or more different types of models using those content items (e.g., natural language inference model, paraphrasing detection model, named entity recognition model). The model training service may then provide the model(s) to a content staleness check (CSC) service. The CSC service may receive, from a client, a request that indicates one or more content items to be checked for staleness. The CSC service may process the content items by the model(s) to generate one or more indications of staleness of the content item. The CSC service may generate a response based on the indications of staleness. For example, the CSC service may generate a document with certain portions highlighted as stale content.

Abstract: Techniques for searching documents are described. An exemplary method includes receiving a document search query; querying at least one index based upon the document search query to identify matching data; fetching the identified matched data; one or more of a top ranked passage and top ranked documents from the set of documents based upon one or more invocations of one or more machine learning models based at least on the fetched identified matched data and the document search query, wherein at least one of the machine learning models has been trained for the third party; and returning one or more of the top ranked passage and the proper subset of documents.