Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

Natural language processing models have emerged that can generate useable software
and automate a number of programming tasks with high fidelity. These tools have yet to …

Optimization is a ubiquitous modeling tool and is often deployed in settings which
repeatedly solve similar instances of the same problem. Amortized optimization methods …

Abstract Machine learning (ML) systems in natural language processing (NLP) face
significant challenges in generalizing to out-of-distribution (OOD) data, where the test …

Deep Neural Networks (DNNs) are ubiquitous and span a variety of applications ranging
from image classification to real-time object detection. As DNN models become more …

Although pre-trained language models~(PLMs) have recently advanced the research
progress in mathematical reasoning, they are not specially designed as a capable multi-task …

Abstract Learning to Optimize (L2O), a technique that utilizes machine learning to learn an
optimization algorithm automatically from data, has gained arising attention in recent years …

Self-explanation is a widely recognized and effective pedagogical method. Previous
research has indicated that self-explanation can be used to evaluate students' …

Large language models have recently shown promising progress in mathematical reasoning
when fine-tuned with human-generated sequences walking through a sequence of solution …

In this paper, we explore the use of large language models to assess human interpretations
of real world events. To do so, we use a language model trained prior to 2020 to artificially …