This paper proposes shortcut decoding, an efficient framework for accelerating Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs). Existing methods that prune or employ early stopping to reduce latency often compromise reasoning reliability. Motivated by the observation that LLMs frequently converge to correct solutions internally before completing explicit textual reasoning, we propose a dual-signal adaptive controller that integrates lightweight probes over internal hidden states with step-level entropy. This controller detects convergence of reasoning during generation and adaptively selects between a fast-exit path and a stability-verified path to remove redundant steps while preserving answer correctness. Experiments across multiple mathematical reasoning benchmarks demonstrate that shortcut decoding reduces token usage by approximately 35%, maintains accuracy comparable to full CoT decoding, and achieves final-answer accuracy comparable to the full CoT baseline, outperforming existing early-stopping methods without updating the base model. Our code is available at https://github.com/kuromi9527/shortcut_decoding.

Recent studies have shown that Pre-trained Language Models (PLMs) are vulnerable to adversarial examples, crafted by introducing human-imperceptible perturbations to clean examples to deceive the models. This vulnerability stems from the divergence in the data distributions of clean and adversarial examples. Therefore, addressing this issue involves teaching the model to diminish the differences between the two types of samples and to focus more on their similarities. To this end, we propose a novel approach named TaiChi that employs a Siamese network architecture. Specifically, it consists of two sub-networks sharing the same structure but trained on clean and adversarial samples, respectively, and uses a contrastive learning strategy to encourage the generation of similar language representations for both kinds of samples. Furthermore, it utilizes the Kullback-Leibler (KL) divergence loss to enhance the consistency in the predictive behavior of the two sub-networks. Extensive experiments across three widely used datasets demonstrate that TaiChi achieves superior trade-offs between robustness to adversarial attacks at token and character levels and accuracy on clean examples compared to previous defense methods. Our code and data are publicly available at https://github.com/sai4july/TaiChi.

As privacy issues are receiving increasing attention within the Natural Language Processing (NLP) community, numerous methods have been proposed to sanitize texts subject to differential privacy. However, the state-of-the-art text sanitization mechanisms based on a relaxed notion of metric local differential privacy (MLDP) do not apply to non-metric semantic similarity measures and cannot achieve good privacy-utility trade-offs. To address these limitations, we propose a novel Customized Text sanitization (CusText) mechanism based on the original 𝜖-differential privacy (DP) definition, which is compatible with any similarity measure.Moreover, CusText assigns each input token a customized output set to provide more advanced privacy protection at the token level.Extensive experiments on several benchmark datasets show that CusText achieves a better trade-off between privacy and utility than existing mechanisms.The code is available at https://github.com/sai4july/CusText.