Due to the cumbersome nature of human evaluation and limitations of code-based
evaluation, Large Language Models (LLMs) are increasingly being used to assist humans in …

The integrity of energy pipelines is crucial for assuring the safe and reliable transportation of
resources. Corrosion defects significantly threaten pipeline infrastructure, necessitating …

Large language models (LLMs) are being increasingly deployed as part of pipelines that
repeatedly process or generate data of some sort. However, a common barrier to …

Operationalizing large language models (LLMs) for custom, repetitive data pipelines is
challenging, particularly due to their unpredictable and potentially catastrophic failures …

The predominant de facto paradigm of testing ML models relies on either using only held-out
data to compute aggregate evaluation metrics or by assessing the performance on different …

ML models are increasingly being pushed to mobile devices, for low-latency inference and
offline operation. However, once the models are deployed, it is hard for ML operators to track …

Software systems that learn from data with AI and machine learning (ML) are becoming
ubiquitous and are increasingly used to automate impactful decisions. The risks arising from …

Reliable simulations are critical for analyzing and understanding complex systems, but their
accuracy depends on correct input data. Incorrect inputs such as invalid or out-of-range …

Accurate ground truth annotation is essential for training and evaluating deep learning
models for remote sensing applications, particularly for tasks such as forest and non-forest …

We present GraphGuard, a data validation framework to improve the data quality of
pipelines to populate knowledge graphs. The inputs for these pipelines often come from …