General circulation models (GCMs) form the backbone of weather and climate prediction, leveraging numerical solvers for large-scale dynamics and parameterizations for smaller-scale processes like cloud formation. Despite continuous improvements, GCMs face significant challenges, including persistent errors, biases, and uncertainties in long-term climate projections and extreme weather events. The recent machine-learning (ML) models have remarkably succeeded… →

In recent years, research on tabular machine learning has grown rapidly. Yet, it still poses significant challenges for researchers and practitioners. Traditionally, academic benchmarks for tabular ML have not fully represented the complexities encountered in real-world industrial applications.  Most available datasets either lack the temporal metadata necessary for time-based splits or come from less extensive… →

Large Language Models (LLMs) excel in various tasks, including text generation, translation, and summarization. However, a growing challenge within NLP is how these models can effectively interact with external tools to perform tasks beyond their inherent capabilities. This challenge is particularly relevant in real-world applications where LLMs must fetch real-time data, perform complex calculations, or… →

Document Visual Question Answering (DocVQA) is a branch of visual question answering that focuses on answering queries about the contents of documents. These documents can take several forms, including scanned photographs, PDFs, and digital documents with text and visual features. However, there are few datasets for DocVQA because collecting and annotating the data is complicated.… →

Recent advances in large language models (LLMs) have made it possible to use LLM agents in many areas, including safety-critical ones like finance, healthcare, and self-driving cars. Usually, these agents use an LLM to understand tasks for making plans, and they can use external tools, like third-party APIs, to carry out those plans. However, their… →

Effectively evaluating document instruction data for training large language models (LLMs) and multimodal large language models (MLLMs) in document visual question answering (VQA) presents a significant challenge. Existing methods are primarily text-oriented, focusing on the textual content of instructions rather than the execution process, which limits their ability to comprehensively assess the quality and efficacy… →