German text simplification requires reducing lexical and structural complexity while preserving meaning, yet standard fine-tuning optimizes reference imitation without directly controlling the predictive distribution where lexical choices are made. We propose VIB-Simp, a lightweight output-logit regularization framework inspired by the Variational Information Bottleneck (VIB) literature. Instead of estimating mutual information or adding latent variables, VIB-Simp regularizes the mean and variance of output logits using a closed-form Gaussian Kullback–Leibler (KL) surrogate, providing a lightweight way to stabilize lexical choices during fine-tuning. An optional unlikelihood-style lexical penalty further suppresses probability mass assigned to difficult lexical candidates.

Experiments on TextComplexityDE and 20Min show consistent gains across LLaMA-3.2-3B, Qwen-2.5-3B, mBART, GerPT-2, and mGPT, with consistent SARI improvements. Additional baselines, length-controlled analysis, ablations, expert ratings, and logit diagnostics show that the gains are not explained by shortening or dictionary-only effects. The results support an IB-style view of simplification: preserve meaning-critical content while suppressing unnecessary lexical complexity.

Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts. As most of these spelling errors are caused by phonetic similarity, effectively modeling the pronunciation of Chinese characters is a key factor for CSC. In this paper, we consider introducing an auxiliary task of Chinese pronunciation prediction (CPP) to improve CSC, and, for the first time, systematically discuss the adaptivity and granularity of this auxiliary task. We propose SCOPE which builds upon a shared encoder two parallel decoders, one for the primary CSC task and the other for a fine-grained auxiliary CPP task, with a novel adaptive weighting scheme to balance the two tasks. In addition, we design a delicate iterative correction strategy for further improvements during inference. Empirical evaluation shows that SCOPE achieves new state-of-the-art on three CSC benchmarks, demonstrating the effectiveness and superiority of the auxiliary CPP task. Comprehensive ablation studies further verify the positive effects of adaptivity and granularity of the task.