  Data contamination has garnered increased attention in the era of large
language models (LLMs) due to the reliance on extensive internet-derived
training corpora. The issue of training corpus overlap with evaluation
benchmarks--referred to as contamination--has been the focus of significant
recent research. This body of work aims to identify contamination, understand
its impacts, and explore mitigation strategies from diverse perspectives.
However, comprehensive studies that provide a clear pathway from foundational
concepts to advanced insights are lacking in this nascent field. Therefore, we
present a comprehensive survey in the field of data contamination, laying out
the key issues, methodologies, and findings to date, and highlighting areas in
need of further research and development. In particular, we begin by examining
the effects of data contamination across various stages and forms. We then
provide a detailed analysis of current contamination detection methods,
categorizing them to highlight their focus, assumptions, strengths, and
limitations. We also discuss mitigation strategies, offering a clear guide for
future research. This survey serves as a succinct overview of the most recent
advancements in data contamination research, providing a straightforward guide
for the benefit of future research endeavors.
