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.

Automatic grammatical error correction is of great value in assisting second language writing. In 2020, the shared task for Chinese grammatical error diagnosis(CGED) was held in NLP-TEA. As the LDU team, we participated the competition and submitted the final results. Our work mainly focused on grammatical error detection, that is, to judge whether a sentence contains grammatical errors. We used the BERT pre-trained model for binary classification, and we achieve 0.0391 in FPR track, ranking the second in all teams. In error detection track, the accuracy, recall and F-1 of our submitted result are 0.9851, 0.7496 and 0.8514 respectively.