Large language model (LLM) performance on reasoning problems typically does not
generalize out of distribution. Previous work has claimed that this can be mitigated with …

There has been considerable divergence of opinion on the reasoning abilities of Large
Language Models (LLMs). While the initial optimism that reasoning might emerge …

Rigorous and reproducible evaluation is critical for assessing the state of the art and for
guiding scientific advances in Artificial Intelligence. Evaluation is challenging in practice due …

A critical factor in the success of many decision support systems is the accurate modeling of
user preferences. Psychology research has demonstrated that users often develop their …

Evaluations are limited by benchmark availability. As models evolve, there is a need to
create benchmarks that can measure progress on new generative capabilities. However …

User alignment is crucial for adapting general-purpose language models (LMs) to
downstream tasks, but human annotations are often not available for all types of instructions …

Enhancing the reasoning capabilities of Large Language Models remains a critical
challenge in artificial intelligence. We introduce RDoLT, Recursive Decomposition of Logical …

Generative AI agents are often expected to respond to complex user requests that have No
One Right Answer (NORA), eg," design a vegetarian meal plan below 1800 calories". Such …

General-purpose language models (LMs) are aligned to diverse user intents, but fall short
when it comes to specific applications. While finetuning is the default method for customized …

Despite the proliferation of machine learning (ML) in a multitude of applications, current
black-box models, such as deep learning, remain hard to understand, critique, and judge by …