Lexical-syntactic flexibility, in the form of conversion (or zero-derivation)
is a hallmark of English morphology. In conversion, a word with one part of
speech is placed in a non-prototypical context, where it is coerced to behave
as if it had a different part of speech. However, while this process affects a
large part of the English lexicon, little work has been done to establish the
degree to which language models capture this type of generalization. This paper
reports the first study on the behavior of large language models with reference
to conversion. We design a task for testing lexical-syntactic flexibility --
the degree to which models can generalize over words in a construction with a
non-prototypical part of speech. This task is situated within a natural
language inference paradigm. We test the abilities of five language models --
two proprietary models (GPT-3.5 and GPT-4), three open-source models (Mistral
7B, Falcon 40B, and Llama 2 70B). We find that GPT-4 performs best on the task,
followed by GPT-3.5, but that the open source language models are also able to
perform it and that the 7B parameter Mistral displays as little difference
between its baseline performance on the natural language inference task and the
non-prototypical syntactic category task, as the massive GPT-4.

大型语言模型对非典型词类构建的词汇 - 句法灵活性方面具有一定的泛化能力，其中 GPT-4 表现最佳。

英语零派生在五个 LLM 中的评估

Verbing Weirds Language (Models): Evaluation of English Zero-Derivation  in Five LLMs

Machine learning has started to be deployed in fields such as healthcare and
finance, which propelled the need for and growth of privacy-preserving machine
learning (PPML). We propose an actively secure four-party protocol (4PC), and a
framework for PPML, showcasing its applications on four of the most
widely-known machine learning algorithms -- Linear Regression, Logistic
Regression, Neural Networks, and Convolutional Neural Networks. Our 4PC
protocol tolerating at most one malicious corruption is practically efficient
as compared to the existing works. We use the protocol to build an efficient
mixed-world framework (Trident) to switch between the Arithmetic, Boolean, and
Garbled worlds. Our framework operates in the offline-online paradigm over
rings and is instantiated in an outsourced setting for machine learning. Also,
we propose conversions especially relevant to privacy-preserving machine
learning. The highlights of our framework include using a minimal number of
expensive circuits overall as compared to ABY3. This can be seen in our
technique for truncation, which does not affect the online cost of
multiplication and removes the need for any circuits in the offline phase. Our
B2A conversion has an improvement of $\mathbf{7} \times$ in rounds and
$\mathbf{18} \times$ in the communication complexity. The practicality of our
framework is argued through improvements in the benchmarking of the
aforementioned algorithms when compared with ABY3. All the protocols are
implemented over a 64-bit ring in both LAN and WAN settings. Our improvements
go up to $\mathbf{187} \times$ for the training phase and $\mathbf{158} \times$
for the prediction phase when observed over LAN and WAN.

提出了一种用于隐私保护机器学习的安全四方协议和框架，在广泛使用的四种机器学习算法上进行了应用，并在离线 - online 模式下操作。实验结果表明，在 LAN 和 WAN 环境下，与现有方法相比，该框架在训练和预测阶段的效果提高了 187 倍和 158 倍。

Trident: 高效的 4PC 框架用于隐私保护机器学习

Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning

Arabizi is Arabic text that is written using Latin characters. Arabizi is
used to present both Modern Standard Arabic (MSA) or Arabic dialects. It is
commonly used in informal settings such as social networking sites and is often
with mixed with English. In this paper we address the problems of: identifying
Arabizi in text and converting it to Arabic characters. We used word and
sequence-level features to identify Arabizi that is mixed with English. We
achieved an identification accuracy of 98.5%. As for conversion, we used
transliteration mining with language modeling to generate equivalent Arabic
text. We achieved 88.7% conversion accuracy, with roughly a third of errors
being spelling and morphological variants of the forms in ground truth.

本文描述了如何使用单词和序列级别的特征来识别英语混杂的 Arabizi 文字，并使用语言建模进行转译，在保真度上达到了 88.7％的识别精度。