20 citations · 22 across the 6 of their papers we have counts for
10 papers
Intent Laundering: AI Safety Datasets Are Not What They Seem
Shahriar Golchin, Marc Wetter
We systematically evaluate the quality of widely used adversarial safety datasets from two perspectives: in isolation and in practice. In isolation, we examine how well these datas…
Towards Compute-Optimal Many-Shot In-Context Learning
Shahriar Golchin, Yanfei Chen, Rujun Han +7
Long-context large language models (LLMs) are able to process inputs containing up to several million tokens. In the scope of in-context learning (ICL), this translates into using…
Using Large Language Models for Automated Grading of Student Writing about Science
Chris Impey, Matthew Wenger, Nikhil Garuda +2
Assessing writing in large classes for formal or informal learners presents a significant challenge. Consequently, most large classes, particularly in science, rely on objective as…
Data Contamination Report from the 2024 CONDA Shared Task
Oscar Sainz, Iker García-Ferrero, Alon Jacovi +25
The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as…
Memorization in In-Context Learning
Shahriar Golchin, Mihai Surdeanu, Steven Bethard +2
In-context learning (ICL) has proven to be an effective strategy for improving the performance of large language models (LLMs) with no additional training. However, the exact mecha…
Grading Massive Open Online Courses Using Large Language Models
Shahriar Golchin, Nikhil Garuda, Christopher Impey +1
Massive open online courses (MOOCs) offer free education globally. Despite this democratization of learning, the massive enrollment in these courses makes it impractical for an ins…