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Found 5 papers, 2 papers with code
Large Language Models As MOOCs Graders
Specifically, we focus on two state-of-the-art LLMs: GPT-4 and GPT-3. 5, across three distinct courses: Introductory Astronomy, Astrobiology, and the History and Philosophy of Astronomy.
Data Contamination Quiz: A Tool to Detect and Estimate Contamination in Large Language Models
We propose the Data Contamination Quiz (DCQ), a simple and effective approach to detect data contamination in large language models (LLMs) and estimate the amount of it.
Time Travel in LLMs: Tracing Data Contamination in Large Language Models
To estimate contamination of individual instances, we employ "guided instruction:" a prompt consisting of the dataset name, partition type, and the random-length initial segment of a reference instance, asking the LLM to complete it.
Do not Mask Randomly: Effective Domain-adaptive Pre-training by Masking In-domain Keywords
We propose a novel task-agnostic in-domain pre-training method that sits between generic pre-training and fine-tuning.
A Compact Pretraining Approach for Neural Language Models
We construct these compact subsets from the unstructured data using a combination of abstractive summaries and extractive keywords.
