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Existing large language model (LLM) evaluation benchmarks primarily focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-linguistic reasoning abilities. This dual limitation makes it…

Ancient Chinese text processing presents unique challenges for large language models (LLMs) due to its distinct linguistic features, complex structural constraints, and rich cultural context. While existing benchmarks have primarily focused…

计算与语言 · 计算机科学 2025-03-21 Shangqing Zhao , Yuhao Zhou , Yupei Ren , Zhe Chen , Chenghao Jia , Fang Zhe , Zhaogaung Long , Shu Liu , Man Lan

Large language models (LLMs) have shown the potential to be integrated into human daily lives. Therefore, user preference is the most critical criterion for assessing LLMs' performance in real-world scenarios. However, existing benchmarks…

计算与语言 · 计算机科学 2023-07-28 Liang Xu , Anqi Li , Lei Zhu , Hang Xue , Changtai Zhu , Kangkang Zhao , Haonan He , Xuanwei Zhang , Qiyue Kang , Zhenzhong Lan

Language models have made remarkable advancements in understanding and generating human language, achieving notable success across a wide array of applications. However, evaluating these models remains a significant challenge, particularly…

计算与语言 · 计算机科学 2025-01-07 M. Ali Bayram , Ali Arda Fincan , Ahmet Semih Gümüş , Banu Diri , Savaş Yıldırım , Öner Aytaş

In the age of large-scale language models, benchmarks like the Massive Multitask Language Understanding (MMLU) have been pivotal in pushing the boundaries of what AI can achieve in language comprehension and reasoning across diverse…

The recently unprecedented advancements in Large Language Models (LLMs) have propelled the medical community by establishing advanced medical-domain models. However, due to the limited collection of medical datasets, there are only a few…

计算与语言 · 计算机科学 2024-06-10 Ping Yu , Kaitao Song , Fengchen He , Ming Chen , Jianfeng Lu

In this paper, we propose a comprehensive evaluation benchmark for Visual Language Models (VLM) in Traditional Chinese. Our evaluation suite, the first of its kind, contains two complementary components: (1) VisTW-MCQ, a collection of…

计算与语言 · 计算机科学 2025-03-18 Zhi Rui Tam , Ya-Ting Pai , Yen-Wei Lee , Yun-Nung Chen

Language models have made significant advancements in understanding and generating human language, achieving remarkable success in various applications. However, evaluating these models remains a challenge, particularly for resource-limited…

计算与语言 · 计算机科学 2025-08-19 M. Ali Bayram , Ali Arda Fincan , Ahmet Semih Gümüş , Banu Diri , Savaş Yıldırım , Öner Aytaş

While the capabilities of Large Language Models (LLMs) have been studied in both Simplified and Traditional Chinese, it is yet unclear whether LLMs exhibit differential performance when prompted in these two variants of written Chinese.…

计算与语言 · 计算机科学 2025-05-29 Hanjia Lyu , Jiebo Luo , Jian Kang , Allison Koenecke

Large Language Models (LLMs) has made significant progress in a number of professional fields, including medicine, law, and finance. However, in traditional Chinese medicine (TCM), there are challenges such as the essential differences…

计算与语言 · 计算机科学 2024-06-25 Heyi Zhang , Xin Wang , Zhaopeng Meng , Zhe Chen , Pengwei Zhuang , Yongzhe Jia , Dawei Xu , Wenbin Guo

In this study, we introduce CT-LLM, a 2B large language model (LLM) that illustrates a pivotal shift towards prioritizing the Chinese language in developing LLMs. Uniquely initiated from scratch, CT-LLM diverges from the conventional…

The evaluation of large language models is an essential task in the field of language understanding and generation. As language models continue to advance, the need for effective benchmarks to assess their performance has become imperative.…

计算与语言 · 计算机科学 2023-10-03 Chan-Jan Hsu , Chang-Le Liu , Feng-Ting Liao , Po-Chun Hsu , Yi-Chang Chen , Da-shan Shiu

Classical Chinese is a gateway to the rich heritage and wisdom of ancient China, yet its complexities pose formidable comprehension barriers for most modern people without specialized knowledge. While Large Language Models (LLMs) have shown…

计算与语言 · 计算机科学 2024-10-01 Jiahuan Cao , Dezhi Peng , Peirong Zhang , Yongxin Shi , Yang Liu , Kai Ding , Lianwen Jin

Large language models (LLMs) are advancing rapidly in medical NLP, yet Traditional Chinese Medicine (TCM) with its distinctive ontology, terminology, and reasoning patterns requires domain-faithful evaluation. Existing TCM benchmarks are…

Despite the rapid development of Chinese vision-language models (VLMs), most existing Chinese vision-language (VL) datasets are constructed on Western-centric images from existing English VL datasets. The cultural bias in the images makes…

计算机视觉与模式识别 · 计算机科学 2024-07-02 Yuxuan Wang , Yijun Liu , Fei Yu , Chen Huang , Kexin Li , Zhiguo Wan , Wanxiang Che

Traditional Chinese Medicine (TCM), as an effective alternative medicine, has been receiving increasing attention. In recent years, the rapid development of large language models (LLMs) tailored for TCM has highlighted the urgent need for…

Large Language Models (LLMs) have demonstrated remarkable capabilities in modern medicine, yet their application in Traditional Chinese Medicine (TCM) remains severely limited by the absence of standardized benchmarks and the scarcity of…

Chinese Large Language Models (LLMs) have recently demonstrated impressive capabilities across various NLP benchmarks and real-world applications. However, the existing benchmarks for comprehensively evaluating these LLMs are still…

计算与语言 · 计算机科学 2024-03-20 Chuang Liu , Renren Jin , Yuqi Ren , Deyi Xiong

Alignment has become a critical step for instruction-tuned Large Language Models (LLMs) to become helpful assistants. However, the effective evaluation of alignment for emerging Chinese LLMs is still largely unexplored. To fill in this gap,…

Large language models (LLMs) have obtained promising results in mathematical reasoning, which is a foundational skill for human intelligence. Most previous studies focus on improving and measuring the performance of LLMs based on textual…

计算与语言 · 计算机科学 2024-11-04 Wentao Liu , Qianjun Pan , Yi Zhang , Zhuo Liu , Ji Wu , Jie Zhou , Aimin Zhou , Qin Chen , Bo Jiang , Liang He