11 citations · 12 across the 5 of their papers we have counts for
5 papers
Towards Large Language Models for Lunar Mission Planning and In Situ Resource Utilization
Michael Pekala, Gregory Canal, Samuel Barham +5
A key factor for lunar mission planning is the ability to assess the local availability of raw materials. However, many potentially relevant measurements are scattered across a var…
To Burst or Not to Burst: Generating and Quantifying Improbable Text
Kuleen Sasse, Samuel Barham, Efsun Sarioglu Kayi +1
While large language models (LLMs) are extremely capable at text generation, their outputs are still distinguishable from human-authored text. We explore this separation across man…
MegaWika: Millions of reports and their sources across 50 diverse languages
Samuel Barham, Orion Weller, Michelle Yuan +9
To foster the development of new models for collaborative AI-assisted report generation, we introduce MegaWika, consisting of 13 million Wikipedia articles in 50 diverse languages,…
Synthetic Cross-language Information Retrieval Training Data
James Mayfield, Eugene Yang, Dawn Lawrie +5
A key stumbling block for neural cross-language information retrieval (CLIR) systems has been the paucity of training data. The appearance of the MS MARCO monolingual training set…
Interpretable Adversarial Training for Text
Samuel Barham, Soheil Feizi
Generating high-quality and interpretable adversarial examples in the text domain is a much more daunting task than it is in the image domain. This is due partly to the discrete na…