Natural Language Processing (NLP) research is increasingly focusing on the
use of Large Language Models (LLMs), with some of the most popular ones being
either fully or partially closed-source. The lack of access to model details,
especially regarding training data, has repeatedly raised concerns about data
contamination among researchers. Several attempts have been made to address
this issue, but they are limited to anecdotal evidence and trial and error.
Additionally, they overlook the problem of \emph{indirect} data leaking, where
models are iteratively improved by using data coming from users. In this work,
we conduct the first systematic analysis of work using OpenAI's GPT-3.5 and
GPT-4, the most prominently used LLMs today, in the context of data
contamination. By analysing 255 papers and considering OpenAI's data usage
policy, we extensively document the amount of data leaked to these models
during the first year after the model's release. We report that these models
have been globally exposed to $\sim$4.7M samples from 263 benchmarks. At the
same time, we document a number of evaluation malpractices emerging in the
reviewed papers, such as unfair or missing baseline comparisons and
reproducibility issues. We release our results as a collaborative project on
this https URL, where other researchers can contribute to our
efforts.

使用 OpenAI 的 GPT-3.5 进行了首次系统分析，揭示其在数据污染方面的问题，发现模型在发布后一年内泄露了大约 470 万个样本来自 263 个基准，并记录了被评审论文中出现的不公平或缺失的基准比较和可复现性问题。

数据污染与评估不端行为在闭源语言模型中的重复现象

Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in  Closed-Source LLMs

This paper examines the comparative effectiveness of a specialized compiled
language model and a general-purpose model like OpenAI's GPT-3.5 in detecting
SDGs within text data. It presents a critical review of Large Language Models
(LLMs), addressing challenges related to bias and sensitivity. The necessity of
specialized training for precise, unbiased analysis is underlined. A case study
using a company descriptions dataset offers insight into the differences
between the GPT-3.5 and the specialized SDG detection model. While GPT-3.5
boasts broader coverage, it may identify SDGs with limited relevance to the
companies' activities. In contrast, the specialized model zeroes in on highly
pertinent SDGs. The importance of thoughtful model selection is emphasized,
taking into account task requirements, cost, complexity, and transparency.
Despite the versatility of LLMs, the use of specialized models is suggested for
tasks demanding precision and accuracy. The study concludes by encouraging
further research to find a balance between the capabilities of LLMs and the
need for domain-specific expertise and interpretability.

该研究比较了一个专门编译的语言模型和通用模型（如 OpenAI 的 GPT-3.5）在检测文本数据中可持续发展目标（SDGs）方面的效果。通过对大型语言模型（LLMs）进行关键性回顾，探讨了与偏见和敏感性相关的挑战。强调了需要专门的训练来进行准确、无偏的分析。使用公司描述数据集的案例研究揭示了 GPT-3.5 和专门的 SDG 检测模型之间的差异。虽然 GPT-3.5 具有更广泛的覆盖范围，但可能针对公司活动的相关性有限地识别出 SDGs。相反，专门的模型更专注于高度相关的 SDGs。强调了深思熟虑的模型选择的重要性，考虑任务需求、成本、复杂性和透明度。尽管 LLMs 非常灵活，但建议在需要精确性和准确性的任务中使用专门的模型。研究最后鼓励进一步研究在 LLMs 的能力与领域特定专业知识和可解释性之间找到平衡。