Abstract: A method includes receiving a query and applying a classifier to a hierarchical taxonomy and the query to output a query taxonomy array. Each of a number of taxonomy tags has an associated level in a hierarchical taxonomy. The query taxonomy array includes query tags selected, based on a term contained in the query, from the taxonomy tags. The query taxonomy array is compared to article tags associated with articles. The article tags are selected from the taxonomy tags. Shared tags are also identified based on sharing. A list of articles is generated, the list including a subset of articles selected from the articles. The subset of articles are associated with the shared tags. Corresponding scores are assigned to the subset of articles in the list. The list is sorted, based on the corresponding scores, to generate a sorted list of articles. The sorted list is presented.

Abstract: Certain aspects of the disclosure provide systems and methods for detecting hallucinations in machine learning models. A method generally includes generating a potential answer from an initial prompt received from a user. The method generally includes interrogating the machine learning model with a verification prompt formulated to elicit a positive or negative response from the machine learning model based on the potential answer and initial prompt. A negative response by the neural network model to the verification prompt is indicative of the potential answer being a hallucination. A positive response by the neural network model to the verification prompt is indicative of the potential answer being free from a hallucination. The method generally includes outputting to the user the potential answer as a final answer upon receiving a positive response to the verification prompt.

Abstract: Certain aspects of the present disclosure provide techniques for managing a search engine based on search performance metrics. An example method generally includes dividing a set of search history data into a first subset of search history data and a second subset of search history data. The first subset of data is associated with interaction with search results, and the second subset of data is associated with non-interaction with search results. A first quality score is generated for searches in the first subset of data. A second quality score is generated for searches in the second subset of data based on different search intents identified for each temporally related group in the second subset of data. An overall quality score is generated for a search engine, and one or more actions with respect to the search engine are taken based on the overall quality score.

Abstract: Certain aspects of the present disclosure provide techniques for managing a search engine based on search performance metrics. An example method generally includes dividing a set of search history data into a first subset of search history data and a second subset of search history data. The first subset of data is associated with interaction with search results, and the second subset of data is associated with non-interaction with search results. A first quality score is generated for searches in the first subset of data. A second quality score is generated for searches in the second subset of data based on different search intents identified for each temporally related group in the second subset of data. An overall quality score is generated for a search engine, and one or more actions with respect to the search engine are taken based on the overall quality score.

Abstract: Systems and methods for a boosted Latent Dirichlet Allocation model with predefined clusters and repelling force. Herein, clusters are pre-populated with seed words with amplified weights to predefine the characteristics of clusters. A repel force is also applied while assigning words to specific clusters in order to assign words more accurately to clusters in instances where clusters have similar characteristics. In addition to generating improved clustering of words, systems and methods may analyze words within queries to provide explainability output related to the performance of content or data.