Aligner can be applied to any powerful, large-scale upstream models. Moreover, it can even iteratively bootstrap the upstream models using corrected responses as synthetic human preference data, breaking through the model's performance ceiling.

In each case, the ambiguity persists even when the database context is provided.

However, most GNNs operate as black-box models and require post-hoc explanations, which may not suffice in high-stakes scenarios where transparency is crucial. In this paper, we present a GNN that is interpretable by design. Our model, Graph Neural Additive Network (GNAN), is a novel extension of the interpretable class of Generalized Additive Models, and can be visualized and fully understood by humans. GNAN is designed to be fully interpretable, offering both global and local explanations at the feature and graph levels through direct visualization of the model.

As language models (LMs) are widely utilized in personalized communication scenarios ( e.g., sending emails, writing social media posts) and endowed with a

The advent of large vision-language models (L VLMs) has spurred research into their applications in multi-modal contexts, particularly in video understanding.