With the involvement of multiple programming languages in modern software development, cross-lingual code clone detection has gained traction within the software engineering community. Numerous studies have explored this topic, proposing various promising approaches. Inspired by the significant advances in machine learning in recent years, particularly Large Language Models (LLMs), which have demonstrated their ability to tackle various tasks, this paper revisits cross-lingual code clone detection. We evaluate the performance of five (05) LLMs and eight prompts (08) for the identification of cross-lingual code clones. Additionally, we compare these results against two baseline methods. Finally, we evaluate a pre-trained embedding model to assess the effectiveness of the generated representations for classifying clone and non-clone pairs. The studies involving LLMs and Embedding models are evaluated using two widely used cross-lingual datasets, XLCoST and CodeNet. Our results show that LLMs can achieve high F1 scores, up to 0.99, for straightforward programming examples. However, they not only perform less well on programs associated with complex programming challenges but also do not necessarily understand the meaning of "code clones" in a cross-lingual setting. We show that embedding models used to represent code fragments from different programming languages in the same representation space enable the training of a basic classifier that outperforms all LLMs by ~1 and ~20 percentage points on the XLCoST and CodeNet datasets, respectively. This finding suggests that, despite the apparent capabilities of LLMs, embeddings provided by embedding models offer suitable representations to achieve state-of-the-art performance in cross-lingual code clone detection.
X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual Instructions
State Key Laboratory of Multimodal Artificial Intelligence Systems（多模态人工智能系统国家重点实验室）；Institute of Automation, CAS（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Evaluating Modern Large Language Models on Low-Resource and Morphologically Rich Languages:A Cross-Lingual Benchmark Across Cantonese, Japanese, and Turkish
University of California, Santa Cruz（加州大学圣克鲁兹分校）；Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh（匹兹堡大学）
Toward Robust Multilingual Adaptation of LLMs for Low-Resource Languages
Department of Automation, Tsinghua University, Beijing, China（清华大学自动化系）；Alibaba International Digital Commerce Group, Beijing, China（阿里巴巴国际数字 commerce 集团）；School of Software, Tsinghua University, Beijing, China（清华大学软件学院）
Investigating Cross-Modal Skill Injection: Scenarios, Methods, and Hyperparameters
State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机科学学院，北京大学）；WeChat AI, Tencent Inc., China（腾讯公司，中国）；The University of Hong Kong（香港大学）
SoAy: A Solution-based LLM API-using Methodology for Academic Information Seeking
Renmin University of China（中国人民大学）；Key Laboratory of Data Engineering and Knowledge Engineering, MOE（数据工程与知识工程重点实验室）；Tsinghua University（清华大学）；Institute of Education（教育学院）；Department of Computer Science and Technology（计算机科学与技术系）；Engineering Research Center of Database and Business Intelligence, MOE（数据库与商务智能工程研究中心）；School of Information（信息学院）
针对学术查询中的复杂 API 耦合问题，提出基于预构建 API 调用方案与代码推理的 SoAy 方法，并通过 SoAyBench/SoAyEval 验证其显著优于现有基线。
Improving MLLM's Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Zhongke Fanyu Technology Co., Ltd（中科泛语科技有限公司）
A Unified Biomedical Named Entity Recognition Framework with Large Language Models
College of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；Air Force Communications NCO Academy（空军通信NCO学院）；Department of Pharmacy, Second Affiliated Hospital of Dalian Medical University（大连医科大学第二附属医院药学部）
GenKnowSub: Improving Modularity and Reusability of LLMs through General Knowledge Subtraction
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran（伊朗德黑兰大学电气与计算机工程学院）；Tehran Institute for Advanced Studies, Khatam University, Tehran, Iran（德黑兰高级研究学院）
Abstract Activation Spaces for Content-Invariant Reasoning in Large Language Models
Human Centric ART, University of Rome Tor Vergata（人类中心ART，罗马大学托尔维加塔分校）；ILCC, School of Informatics, University of Edinburgh（ILCC，信息学院，爱丁堡大学）；School of Computer Science, University of Sheffield（计算机科学学院，谢菲尔德大学）；Almawave S.p.A.（Almawave公司）
Almieyar-Oryx-BloomBench: A Bilingual Multimodal Benchmark for Cognitively Informed Evaluation of Vision-Language Models
University of British Columbia（不列颠哥伦比亚大学）；Zuse School（Zuse学校）；Qatar Computing Research Institute (QCRI)（卡塔尔计算研究所）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）
Macro: Enhancing Multilingual Counterfactual Explanations through Alignment-as-Preference Optimization
Technische Universität Berlin（柏林技术大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；University of Duisburg-Essen（杜伊斯堡- Essen大学）；LMU Munich（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Saarland Informatics Campus（萨尔兰州信息学校区）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究院）；Centre for European Research in Trusted AI (CERTAIN)（可信人工智能欧洲研究中心）
