Comparative reasoning plays a crucial role in predicting text preferences; however, large
language models (LLMs) often demonstrate inconsistencies in their reasoning, leading to …

Abstract Machine learning models can perform well on in-distribution data but often fail on
biased subgroups that are underrepresented in the training data hindering the robustness of …

Deep learning models are effective, yet brittle. Even carefully trained, their behavior tends to
be hard to predict when confronted with out-of-distribution samples. In this work, our goal is …

Large language models (LLMs) often exhibit subtle yet distinctive characteristics in their
outputs that users intuitively recognize, but struggle to quantify. These" vibes"--such as tone …

With models getting stronger, evaluations have grown more complex, testing multiple skills
in one benchmark and even in the same instance at once. However, skill-wise performance …

There has been considerable recent interest in interpretable concept-based models such as
Concept Bottleneck Models (CBMs), which first predict human-interpretable concepts and …

Transfer learning aims to transfer knowledge or information from a source domain to a
relevant target domain. In this paper, we understand transfer learning from the perspectives …

Concept Bottleneck Models (CBMs) have been proposed as a compromise between white-
box and black-box models, aiming to achieve interpretability without sacrificing accuracy …

Vision Language Models (VLMs) are typically evaluated with Visual Question Answering
(VQA) tasks which assess a model's understanding of scenes. Good VQA performance is …

To make sense of massive data, we often fit simplified models and then interpret the
parameters; for example, we cluster the text embeddings and then interpret the mean …