Training data memorization in language models impacts model capability
(generalization) and safety (privacy risk). This paper focuses on analyzing
prompts' impact on detecting the memorization of 6 masked language model-based
named entity recognition models. Specifically, we employ a diverse set of 400
automatically generated prompts, and a pairwise dataset where each pair
consists of one person's name from the training set and another name out of the
set. A prompt completed with a person's name serves as input for getting the
model's confidence in predicting this name. Finally, the prompt performance of
detecting model memorization is quantified by the percentage of name pairs for
which the model has higher confidence for the name from the training set. We
show that the performance of different prompts varies by as much as 16
percentage points on the same model, and prompt engineering further increases
the gap. Moreover, our experiments demonstrate that prompt performance is
model-dependent but does generalize across different name sets. A comprehensive
analysis indicates how prompt performance is influenced by prompt properties,
contained tokens, and the model's self-attention weights on the prompt.

该研究分析了提示对检测 6 个基于掩码语言模型的命名实体识别模型记忆作用的影响，并通过使用多样的 400 个自动生成的提示集和数据对模型在训练集内和训练集外的名称对的置信度进行了量化。结果显示，不同的提示在同一模型上的性能差异高达 16 个百分点，并且通过优化提示进一步增加了差距。此外，实验证明提示性能依赖于模型，但在不同的名称集合中具有普适性。全面分析表明提示性能受提示属性、包含的标记以及模型对提示的自注意权重的影响。

基于掩码语言模型的命名实体识别中激发记忆的提示探索

Exploring prompts to elicit memorization in masked language model-based  named entity recognition

With the introduction of ChatGPT, OpenAI made large language models (LLM)
accessible to users with limited IT expertise. However, users with no
background in natural language processing (NLP) might lack a proper
understanding of LLMs. Thus the awareness of their inherent limitations, and
therefore will take the systems' output at face value. In this paper, we
systematically analyse prompts and the generated responses to identify possible
problematic issues with a special focus on gender biases, which users need to
be aware of when processing the system's output. We explore how ChatGPT reacts
in English and German if prompted to answer from a female, male, or neutral
perspective. In an in-depth investigation, we examine selected prompts and
analyse to what extent responses differ if the system is prompted several times
in an identical way. On this basis, we show that ChatGPT is indeed useful for
helping non-IT users draft texts for their daily work. However, it is
absolutely crucial to thoroughly check the system's responses for biases as
well as for syntactic and grammatical mistakes.

分析 ChatGPT 对有性别偏见和提示分析的系统回应，检查其可用性和限制。