Abstract: A molecule representation is extracted from a document and associated with the document in a metadata database. For example, an image of a molecular structure may be extracted from a document and stored in the metadata database in a text-based representation such as SMILES. The metadata database may be searched to identify documents that mention a particular molecule. Continuing the example, the metadata database may be searched with a SMILES representation to identify the document and other documents that refer to the same molecule. The metadata database may index documents based on different types of molecule representations, including text-based, image-based, graph-based, name, abbreviation, etc. This allows search over multiple representations of a molecule, improving accuracy and thoroughness. These improvements reduce the time and computational resources needed to search for documents that refer to a particular molecule.

Abstract: The present disclosure relates to efficiently receiving and processing input tasks in a way that is scalable and which reduces both the quantity of tokens processed by a foundation model (e.g., an LLM) as well as the number of API calls that are made in processing the input tasks. A system batches a set of inputs to provide as a single batch of input(s) into an LLM. The system generates one or more permutations of the batched input(s) to determine outputs based on variable orders in which the input data is provided within the respective permutations of the batched inputs. The system further may eliminate one or more of the data inputs within the respective batches to facilitate smaller batched inputs without sacrificing accuracy in a set of outputs generated by the LLM responsive to the batch permutations.

Abstract: The present disclosure relates to methods and systems for sharing with a plurality of users a chat session that uses large language models to provide responses for input messages received for the chat session. The methods and systems provide access to the chat session to the users and update the chat session in response to any changes made to the chat session by any of the users. The methods and systems allow the users to resume the chat session at a future time using the chat session history.

Abstract: The present disclosure relates to methods and systems for sharing with a plurality of users a chat session that uses large language models to provide responses for input messages received for the chat session. The methods and systems provide access to the chat session to the users and update the chat session in response to any changes made to the chat session by any of the users. The methods and systems allow the users to resume the chat session at a future time using the chat session history.

Abstract: The present disclosure relates to methods and systems for automatically generating documents for a specific topic using large language models. The methods and systems receive an input query that identifies a topic for the document. The methods and systems automatically generate, using the large language models, a framework for the document with sections and subsections for the document. The methods and systems write the document, using the large language models, and provide references for the data sources used to obtain the data that the large language model used to write the document.

Abstract: A data processing system implements obtaining query parameters for a query for content items in a datastore, the query parameters including attributes of content items for which a search is to be conducted; obtaining a first set of content items from a content datastore based on the query parameters; analyzing the first set of content items using a first machine learning model trained to generate relevant content information that identifies a plurality of relevant content items included in the first set of content items; and analyzing the plurality of relevant content items using a second machine learning model configured to output novel content information, the novel content information including a plurality of content items predicted to be relevant and novel, the novel content information ranking the plurality of content items predicted to be relevant and novel based on a novelty score associated with each respective content item.

Abstract: A data processing system implements obtaining query parameters for a query for content items in a datastore, the query parameters including attributes of content items for which a search is to be conducted; obtaining a first set of content items from a content datastore based on the query parameters; analyzing the first set of content items using a first machine learning model trained to generate relevant content information that identifies a plurality of relevant content items included in the first set of content items; and analyzing the plurality of relevant content items using a second machine learning model configured to output novel content information, the novel content information including a plurality of content items predicted to be relevant and novel, the novel content information ranking the plurality of content items predicted to be relevant and novel based on a novelty score associated with each respective content item.

Abstract: A computer implemented method includes receiving an image that includes a type of object, segmenting the object into multiple segments via a trained segmentation machine learning model, and inputting the segments into multiple different attribute extraction models to extract different types of attributes from each of the multiple segments.