The Toronto-dominion Bank

Jesse Cole Cresswell

Yaqiao Luo

Atiyeh Ashari Ghomi

Maksims Volkovs

Maria Esipova

Paige Elyse Dickie

Guangwei Yu

Kin Kwan Leung

Barum Rho

Bradley Craig Anderson Brown

Anthony Lawrence Caterini

Ilan Gofman

Gabriel Loaiza Ganem

Jiapeng Wu

Kai Wang

Caitlin Messick

Brendan Leigh Ross

Lu Shu

A model evaluation system evaluates the extent to which privacy-aware training processes affect the direction of training gradients for groups. A modified differential-privacy ('DP') training process provides per-sample gradient adjustments with parameters that may be adaptively modified for different data batches. Per-sample gradients are modified with respect to a reference bound and a clipping bound. A scaling factor may be determined for each per-sample gradient based on the higher of the reference bound or a magnitude of the per-sample gradient. Per-sample gradients may then be adjusted based on a ratio of the clipping bound to the scaling factor. A relative privacy cost between groups may be determined as excess training risk based on a difference in group gradient direction relative to an unadjusted batch gradient and the adjusted batch gradient according to the privacy-aware training.

Identifying and mitigating disparate group impact in differential-privacy machine-learned models

A computer model is trained to account for data samples in a high-dimensional space as lying on different manifolds, rather than a single manifold to represent the data set, accounting for the data set as a whole as a union of manifolds. Different data samples that may be expected to belong to the same underlying manifold are determined by grouping the data. For generative models, a generative model may be trained that includes a sub-model for each group trained on that group's data samples, such that each sub-model can account for the manifold of that group. The overall generative model includes information describing the frequency to sample from each sub-model to correctly represent the data set as a whole in sampling. Multi-class classification models may also use the grouping to improve classification accuracy by weighing group data samples according to the estimated latent dimensionality of the group.

Modeling disjoint manifolds

A computing system uses a large language model (LLM) to generate one or more synthetic queries for each document of a set of documents. For a user query, the computing system: selects one or more of the synthetic queries related to the user query; generates an adaptive few-shot prompt to instruct the LLM to generate a response to the query, wherein the adaptive few-shot prompt comprises an example query-response pair for each of the selected one more synthetic queries; provides the adaptive few-shot prompt to the LLM as an input; and generates an amended query based on the output of the LLM in response to the adaptive few-shot prompt.

Computing systems and methods for query expansion for use in information retrieval

The disclosed embodiments include computer-implemented processes that generate adaptive textual explanations of output using trained artificial intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with a first temporal interval, and based on an application of a trained artificial intelligence process to the input dataset, generate output data representative of a predicted likelihood of an occurrence of an event during a second temporal interval. Further, and based on an application of a trained explainability process to the input dataset, the apparatus may generate an element of textual content that characterizes an outcome associated with the predicted likelihood of the occurrence of the event, where the element of textual content is associated with a feature value of the input dataset. The apparatus may also transmit a portion of the output data and the element of textual content to a computing system.

Generating adaptive textual explanations of output predicted by trained artificial-intelligence processes

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Toronto, Canada

Jesse Cole Cresswell