6 papers
FlowCheck: Helping End-Users Specify and Verify Intent in Vibe-Coded Web Apps
Reya Vir, Lydia Chilton, Zhuo Zhang +1
Vibe-coded applications often contain silent behavioral failures in which the interface appears functional even though user-visible information does not flow to the expected state…
LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lake
Haonan Wang, Jiaxiang Liu, Yurong Liu +11
Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can be trivially retrieved. In cont…
Subliminal Corruption: Mechanisms, Thresholds, and Interpretability
Reya Vir, Sarvesh Bhatnagar
As machine learning models are increasingly fine-tuned on synthetic data, there is a critical risk of subtle misalignments spreading through interconnected AI systems. This paper i…
PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines
Reya Vir, Shreya Shankar, Harrison Chase +2
Large language models (LLMs) are increasingly deployed in specialized production data processing pipelines across diverse domains -- such as finance, marketing, and e-commerce. How…
Polynomial Regression as a Task for Understanding In-context Learning Through Finetuning and Alignment
Max Wilcoxson, Morten Svendgård, Ria Doshi +3
Simple function classes have emerged as toy problems to better understand in-context-learning in transformer-based architectures used for large language models. But previously prop…
Can Transformers Learn Sequential Function Classes In Context?
Ryan Campbell, Emma Guo, Evan Hu +2
In-context learning (ICL) has revolutionized the capabilities of transformer models in NLP. In our project, we extend the understanding of the mechanisms underpinning ICL by explor…