Abstract: Embodiments relate to an automatic detection of fraudulent behavior for a transaction at an online system. The online system requests a large language model (LLM) to determine, based on a prompt input into the LLM, information about a refund event for a first order placed by a user of the online system. The online system accesses a computer model trained to detect a fraudulent behavior associated with an order placed with the online system. The online system applies the computer model to determine a score associated with the refund event, based on the information about the refund event received from the LLM. The online system determines, based on the score, whether the refund event was due to a fraudulent behavior of the user. The online system performs at least one action associated with the online system, based on the determination whether the refund event was due to the fraudulent behavior.

Abstract: An online concierge system receives orders and allocates orders to pickers who obtain items in an order from a retailer and deliver the items to a customer from whom the order was received. When an item included in an order is unavailable, the online concierge system suggests one or more replacement items for the item. To select a replacement item for an unavailable item, the online concierge system uses a set of models trained to predict a probability or each of a set of events, including one or more negative events, for a candidate replacement item. The online concierge system generates a score for a candidate replacement item as a weighted combination of the predicted probabilities, with negative weights applied to the probabilities for negative events. Based on the scores for various candidate replacement products, the online concierge system selects one or more candidate replacement items for an item.

Abstract: Embodiments relate to an automatic detection of fraudulent behavior for a transaction at an online system. The online system requests a large language model (LLM) to determine, based on a prompt input into the LLM, information about a refund event for a first order placed by a user of the online system. The online system accesses a computer model trained to detect a fraudulent behavior associated with an order placed with the online system. The online system applies the computer model to determine a score associated with the refund event, based on the information about the refund event received from the LLM. The online system determines, based on the score, whether the refund event was due to a fraudulent behavior of the user. The online system performs at least one action associated with the online system, based on the determination whether the refund event was due to the fraudulent behavior.

Abstract: A computer system receives an image from a picker, which indicates an out-of-stock target item and potential replacements items. The system provides, to a machine learning model, a prompt requesting identification of the target item and the potential replacement items in the image. The system receives identification of the target item and a list of potential replacement items in the image. The system generates a first list of potential replacements items based on the list of potential replacement items identified in the image and a second list of replacement items from the target item by applying one or more replacement models to the identified target item. The system may merge the two lists and assign replacement scores to each item in the merged list to create a list of recommended replacement items. The system generates a message based on the image and the list of recommended replacement items.