Large language models have shown strong generative and reasoning capabilities, yet they still struggle with natural language to first order logic (NL2FOL) translation due to logical hallucination. We propose LSEG (Logic Structure and Entropy Guided), a fine-tuning free framework designed to improve logical consistency during inference. The core idea of LSEG is to correct hidden state deviation by leveraging logical stability across logic preserving perturbations of the input. Such deviation is especially harmful in NL2FOL, as even small drifts can flip quantifier scope or logical operators, producing formulas that are syntactically valid yet logically incorrect. First, LSEG constructs perturbation-averaged direction vectors that approximate a stable logical center. Second, it derives layer-wise correction directions by contrasting original and perturbed representations. Lastly, LSEG uses an entropy-guided adaptive mechanism to inject these directions only when the model exhibits unstable or over-confident reasoning states, thereby preserving fluency while correcting logical drift. Experiments on the FOLIO and MALLS benchmarks show that LSEG consistently improves logical equivalence scores over strong baselines, despite requiring no training or parameter updates. Further evaluation on LogicLLaMA demonstrates LSEG’s architecture-agnostic effectiveness.

Sentence Compression (SC), which aims to shorten sentences while retaining important words that express the essential meanings, has been studied for many years in many languages, especially in English. However, improvements on Chinese SC task are still quite few due to several difficulties: scarce of parallel corpora, different segmentation granularity of Chinese sentences, and imperfect performance of syntactic analyses. Furthermore, entire neural Chinese SC models have been under-investigated so far. In this work, we construct an SC dataset of Chinese colloquial sentences from a real-life question answering system in the telecommunication domain, and then, we propose a neural Chinese SC model enhanced with a Self-Organizing Map (SOM-NCSCM), to gain a valuable insight from the data and improve the performance of the whole neural Chinese SC model in a valid manner. Experimental results show that our SOM-NCSCM can significantly benefit from the deep investigation of similarity among data, and achieve a promising F1 score of 89.655 and BLEU4 score of 70.116, which also provides a baseline for further research on Chinese SC task.