From the 1 of 5 linked papers with an AI index.
5 papers
Correcting Mode Collapse in Silicon Sampling with Semantic Similarity Rating
Oscar Heath, Rohan Alexander
The paper studies why large language models produce overly uniform survey answers (mode collapse) and proposes using semantic similarity ratings of text-only responses, mapped to n…
Same Prompt, Different Outcomes: Evaluating the Reproducibility of Data Analysis by LLMs
Jiaxin Cui, Rohan Alexander
We systematically evaluate the reproducibility of data analysis conducted by Large Language Models (LLMs). We evaluate two prompting strategies, six models, and four temperature se…
Benchmarking AI Performance on End-to-End Data Science Projects
Evelyn Hughes, Rohan Alexander
Data science is an integrated workflow of technical, analytical, communication, and ethical skills, but current AI benchmarks focus mostly on constituent parts. We test whether AI…
Prompting the Professoriate: A Qualitative Study of Instructor Perspectives on LLMs in Data Science Education
Ana Elisa Lopez-Miranda, Tiffany Timbers, Rohan Alexander
Large Language Models (LLMs) have shifted in just a few years from novelty to ubiquity, raising fundamental questions for data science education. Tasks once used to teach coding, w…
Limits to AI Growth: The Ecological and Social Consequences of Scaling
Eshta Bhardwaj, Rohan Alexander, Christoph Becker
The accelerating development and deployment of AI technologies depend on the continued ability to scale their infrastructure. This has implied increasing amounts of monetary invest…