
The author explores the issue of data contamination and memorization in Large Language Models (LLMs) when applied to tabular data, highlighting the need for ensuring data integrity. The approach involves testing for knowledge, learning, and memorization in LLMs on various datasets.


coremsg

analyzing-memorization-of-tabular-data-by-language-models


Analyzing Memorization of Tabular Data by Language Models


title_rewrite


Instruction-based prompts reveal higher memorization levels in instruction-tuned language models compared to base models.


alpaca-vs-vicuna-uncovering-memorization-in-instruction-tuned-llms


Alpaca vs Vicuna: Uncovering Memorization in Instruction-Tuned LLMs
