"The 24th Chinese Computational Linguistics Conference (CCL25-Eval) features 12 technical evaluation tasks. Among them, Task 7 is the Chinese Literary Language Understanding Evaluation (ZhengMing). ZhengMing is a universal and scalable evaluation framework designed to assess natural language processing (NLP) tasks in the literary domain, such as text classification, text generation, automated question answering, relation extraction, and machine translation.ZhengMing framework aims to evaluate the performance of large language models (LLMs) in the literary field at a fine-grained level. In this mission, 89 teams signed up for the competition, with5 teams ultimately submitting results. The highest score achieved is 0.65. This paper presents and discusses the dataset, task descriptions, competition results, and other relevant information for this evaluation task. This paper introduces and presents relevant information about this evaluation task, including the dataset, task description, and competition results. More details are available at https://github.com/isShayulajiao/CCL25-Eval-ZhengMing."

This paper describes Lan-Bridge Translation systems for the WMT 2023 General Translation shared task. We participate in 2 directions: English to and from Chinese. With the emergence of large-scale models, various industries have undergone significant transformations, particularly in the realm of document-level machine translation. This has introduced a novel research paradigm that we have embraced in our participation in the WMT23 competition. Focusing on advancements in models such as GPT-3.5 and GPT-4, we have undertaken numerous prompt-based experiments. Our objective is to achieve optimal human evaluation results for document-level machine translation, resulting in our submission of the final outcomes in the general track.

This paper describes Lan-Bridge Translation systems for the WMT 2022 General Translation shared task. We participate in 18 language directions: English to and from Czech, German, Ukrainian, Japanese, Russian, Chinese, English to Croatian, French to German, Yakut to and from Russian and Ukrainian to and from Czech.To develop systems covering all these direc_x0002_tions, we mainly focus on multilingual mod_x0002_els. In general, we apply data corpus filtering, scaling model size, sparse expert model (in par_x0002_ticular, Transformer with adapters), large scale backtranslation and language model rerankingtechniques. Our system ranks first in 6 directions based on automatic evaluation.