Ensuring the resilience of Large Language Models (LLMs) against malicious exploitation is paramount, with recent focus on mitigating offensive responses. Yet, the understanding of cant or dark jargon remains unexplored. This paper introduces a domain-specific Cant dataset and CantCounter evaluation framework, employing Fine-Tuning, Co-Tuning, Data-Diffusion, and Data-Analysis stages. Experiments reveal LLMs, including ChatGPT, are susceptible to cant bypassing filters, with varying recognition accuracy influenced by question types, setups, and prompt clues. Updated models exhibit higher acceptance rates for cant queries. Moreover, LLM reactions differ across domains, e.g., reluctance to engage in racism versus LGBT topics. These findings underscore LLMs' understanding of cant and reflect training data characteristics and vendor approaches to sensitive topics. Additionally, we assess LLMs' ability to demonstrate reasoning capabilities. Access to our datasets and code is available at https://github.com/cistineup/CantCounter.

Cant is important for understanding advertising, comedies and dog-whistle politics. However, computational research on cant is hindered by a lack of available datasets. In this paper, we propose a large and diverse Chinese dataset for creating and understanding cant from a computational linguistics perspective. We formulate a task for cant understanding and provide both quantitative and qualitative analysis for tested word embedding similarity and pretrained language models. Experiments suggest that such a task requires deep language understanding, common sense, and world knowledge and thus can be a good testbed for pretrained language models and help models perform better on other tasks. The code is available at https://github.com/JetRunner/dogwhistle. The data and leaderboard are available at https://competitions.codalab.org/competitions/30451.

Manually optimizing artificial neural network (ANN) structures has been an obstacle to the advancement of machine learning as it is significantly time-consuming and requires a considerable level of domain expertise [1]. The structure of an ANN is typically chosen based on its reputation based on results of existent literature or based on knowledge shared across the machine learning community, however changing even a few problem-specific meta-parameters can lead to poor generalization upon committing to a specific topology [2, 3]. To address these challenges, a number of neural architecture search (NAS) [1, 4-8] and neuroevolution (NE) [9, 10] algorithms have been developed to automate the process of ANN design. More recently, nature-inspired neural architecture search (NINAS) algorithms have shown increasing promise, including the Artificial Bee Colony (ABC) optimization procedure [11], the Bat algorithm [12], the Firefly algorithm [13], and the Cuckoo Search algorithm [14]. Among the more recently successful applied NINAS strategies are those based on ant colony optimization (ACO) [15], which have proven to be particularly powerful when automating the design of recurrent neural networks (RNNs). Originally, ACO for NAS was limited to small structures based on Jordan and Elman RNNs [16] or was used as a process for reducing the number of network inputs [17]. Later work proposed generalizations of ACO for optimizing the synaptic connections of RNN memory cell structures [18] and even entire RNN architectures in an algorithmic framework called Ant-based Neural Topology Search (ANTS) [19]. In the ANTS process, ants traverse a single massively-connected "superstructure", which contains all of the possible ways that the nodes of an RNN may connect with each other, both in terms of structure (i.e., all possible feed forward connections), and in time (i.e., all possible recurrent synapses that span many different time delays), searching for optimal RNN sub-networks.