Text data has become extremely valuable due to the emergence of machine
learning algorithms that learn from it. A lot of high-quality text data
generated in the real world is private and therefore cannot be shared or used
freely due to privacy concerns. Generating synthetic replicas of private text
data with a formal privacy guarantee, i.e., differential privacy (DP), offers a
promising and scalable solution. However, existing methods necessitate DP
finetuning of large language models (LLMs) on private data to generate DP
synthetic data. This approach is not viable for proprietary LLMs (e.g.,
GPT-3.5) and also demands considerable computational resources for open-source
LLMs. Lin et al. (2024) recently introduced the Private Evolution (PE)
algorithm to generate DP synthetic images with only API access to diffusion
models. In this work, we propose an augmented PE algorithm, named Aug-PE, that
applies to the complex setting of text. We use API access to an LLM and
generate DP synthetic text without any model training. We conduct comprehensive
experiments on three benchmark datasets. Our results demonstrate that Aug-PE
produces DP synthetic text that yields competitive utility with the SOTA DP
finetuning baselines. This underscores the feasibility of relying solely on API
access of LLMs to produce high-quality DP synthetic texts, thereby facilitating
more accessible routes to privacy-preserving LLM applications. Our code and
data are available at this https URL

我们提出了一种名为 Aug-PE 的增强版 PE 算法，应用于文本的复杂情境，通过 API 访问大型语言模型，生成差分隐私的合成文本，实验证明 Aug-PE 可以产生具有竞争性效用的差分隐私合成文本，从而便捷地在隐私保护的语言模型应用中提供更可访问的路线。

通过基础模型 API 生成差分隐私合成数据 2: 文本

Differentially Private Synthetic Data via Foundation Model APIs 2: Text

Recent work has shown it is possible to construct adversarial examples that
cause an aligned language model to emit harmful strings or perform harmful
behavior. Existing attacks work either in the white-box setting (with full
access to the model weights), or through transferability: the phenomenon that
adversarial examples crafted on one model often remain effective on other
models. We improve on prior work with a query-based attack that leverages API
access to a remote language model to construct adversarial examples that cause
the model to emit harmful strings with (much) higher probability than with
transfer-only attacks. We validate our attack on GPT-3.5 and OpenAI's safety
classifier; we can cause GPT-3.5 to emit harmful strings that current transfer
attacks fail at, and we can evade the safety classifier with nearly 100%
probability.

通过使用具有 API 访问的远程语言模型构建具有更高概率发出有害字符串的对抗性示例，我们改进了之前的工作，并验证了我们的攻击在 GPT-3.5 和 OpenAI 的安全分类器上的有效性。

基于查询的对抗性提示生成

Query-Based Adversarial Prompt Generation

Due to privacy or commercial constraints, large pre-trained language models
(PLMs) are often offered as black-box APIs. Fine-tuning such models to
downstream tasks is challenging because one can neither access the model's
internal representations nor propagate gradients through it. This paper
addresses these challenges by developing techniques for adapting PLMs with only
API access. Building on recent work on soft prompt tuning, we develop methods
to tune the soft prompts without requiring gradient computation. Further, we
develop extensions that in addition to not requiring gradients also do not need
to access any internal representation of the PLM beyond the input embeddings.
Moreover, instead of learning a single prompt, our methods learn a distribution
over prompts allowing us to quantify predictive uncertainty. Ours is the first
work to consider uncertainty in prompts when only having API access to the PLM.
Finally, through extensive experiments, we carefully vet the proposed methods
and find them competitive with (and sometimes even improving on) gradient-based
approaches with full access to the PLM.

本文提出了一种适应预训练语言模型的技术，这种技术在只有 API 访问的情况下，通过软提示调整的方法进行微调，并且不需要访问 PLM 的任何内部表示，同时学习的提示分布可以量化预测的不确定性。通过大量实验证明这种方法可以和基于梯度的完全访问 PLM 方法相竞争甚至超过它们。

可靠的免梯度和免似然函数提示调整

Reliable Gradient-free and Likelihood-free Prompt Tuning

Along with the advent of deep neural networks came various methods of
exploitation, such as fooling the classifier or contaminating its training
data. Another such attack is known as model extraction, where provided API
access to some black box neural network, the adversary extracts the underlying
model. This is done by querying the model in such a way that the underlying
neural network provides enough information to the adversary to be
reconstructed. While several works have achieved impressive results with neural
network extraction in the propositional domain, this problem has not yet been
considered over the relational domain, where data samples are no longer
considered to be independent and identically distributed (iid). Graph Neural
Networks (GNNs) are a popular deep learning framework to perform machine
learning tasks over relational data. In this work, we formalize an instance of
GNN extraction, present a solution with preliminary results, and discuss our
assumptions and future directions.

本文提出了在基于图神经网络的机器学习模型中进行模型抽取的方法，以解决对关系数据中的深度神经网络进行攻击的问题。