Coded language is an important part of human communication.It refers to cases where users intentionally encode meaning so that the surface text differs from the intended meaning and must be decoded to be understood.Current language models handle coded language poorly.Progress has been limited by the lack of real-world datasets and clear taxonomies.This paper introduces CODEDLANG, a dataset of 7,744 Chinese Google Maps reviews, including 900 reviews with span-level annotations of coded language.We developed a seven-class taxonomy that captures common encoding strategies, including phonetic, orthographic, and cross-lingual substitutions.We benchmarked language models on coded language detection, classification, and review rating prediction.Results show that even strong models can fail to identify or understand coded language.Because many coded expressions rely on pronunciation-based strategies, we further conducted a phonetic analysis of coded and decoded forms.Our code and dataset are publicly available 1 .Together, our results highlight coded language as an important and underexplored challenge for real-world NLP systems.

Zhecheng Sheng, Xiruo Ding, Brian Hur, Changye Li, Trevor Cohen, Serguei V. S. Pakhomov. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Deep learning (DL) techniques involving finetuning large numbers of model parameters have delivered impressive performance on the task of discriminating between language produced by cognitively healthy individuals, and those with Alzheimer's disease (AD).However, questions remain about their ability to generalize beyond the small reference sets that are publicly available for research.As an alternative to fitting model parameters directly, we propose a novel method by which a Transformer DL model (GPT-2) pre-trained on general English text is paired with an artificially degraded version of itself (GPT-D), to compute the ratio between these two models' perplexities on language from cognitively healthy and impaired individuals.This technique approaches state-ofthe-art performance on text data from a widely used "Cookie Theft" picture description task, and unlike established alternatives also generalizes well to spontaneous conversations.Furthermore, GPT-D generates text with characteristics known to be associated with AD, demonstrating the induction of dementia-related linguistic anomalies.Our study is a step toward better understanding of the relationships between the inner workings of generative neural language models, the language that they produce, and the deleterious effects of dementia on human speech and language characteristics.