The rapid development of Large Language Models (LLMs) like GPT-4, Claude-3, and
Gemini has transformed the field of natural language processing. However, it has also …

With the rise of Large Language Models (LLMs) in recent years, abundant new opportunities
are emerging, but also new challenges, among which contamination is quickly becoming …

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 …

Recent advancements in Large Language Models (LLMs) have demonstrated significant
progress in various areas, such as text generation and code synthesis. However, the …

Data contamination has received increasing attention in the era of large language models
(LLMs) due to their reliance on vast Internet-derived training corpora. To mitigate the risk of …

Large language models (LLMs) have demonstrated great performance across various
benchmarks, showing potential as general-purpose task solvers. However, as LLMs are …

Test contamination is a serious problem for the evaluation of large language models (LLMs)
because it leads to the overestimation of their performance and a quick saturation of …

Relational databases play an important role in business, science, and beyond. However, the
operability of relational databases is restricted to users familiar with specific languages such …

Earlier works have been showing the efficacy of reasoning methods in eliciting step-wise
reasoning of large language models (LLMs) by operating via in-context demonstrations …

An Efficient strategy for conducting pre-training of language models is the concatenation of
contiguous sequences of text of fixed length through causal masking that estimates the …