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Large Language Models (LLMs) have shown impressive results in multiple domains of natural language processing (NLP) but are mainly focused on the English language. Recently, more LLMs have incorporated a larger proportion of multilingual…

Probing techniques for large language models (LLMs) have primarily focused on English, overlooking the vast majority of the world's languages. In this paper, we extend these probing methods to a multilingual context, investigating the…

计算与语言 · 计算机科学 2025-02-03 Daoyang Li , Haiyan Zhao , Qingcheng Zeng , Mengnan Du

Language modeling has witnessed remarkable advancements in recent years, with Large Language Models (LLMs) like ChatGPT setting unparalleled benchmarks in human-like text generation. However, a prevailing limitation is the…

计算与语言 · 计算机科学 2023-11-13 Abhinand Balachandran

Each new generation of English-oriented Large Language Models (LLMs) exhibits enhanced cross-lingual transfer capabilities and significantly outperforms older LLMs on low-resource languages. This prompts the question: Is there a need for…

计算与语言 · 计算机科学 2024-12-16 Tamzeed Mahfuz , Satak Kumar Dey , Ruwad Naswan , Hasnaen Adil , Khondker Salman Sayeed , Haz Sameen Shahgir

Large Language Models (LLMs) have demonstrated impressive capabilities in language generation and general task performance. However, their application to spoken language understanding (SLU) remains challenging, particularly for token-level…

计算与语言 · 计算机科学 2025-10-09 Shangjian Yin , Peijie Huang , Jiatian Chen , Haojing Huang , Yuhong Xu

Rapid developments of large language models have revolutionized many NLP tasks for English data. Unfortunately, the models and their evaluations for low-resource languages are being overlooked, especially for languages in South Asia.…

计算与语言 · 计算机科学 2025-09-16 Sampoorna Poria , Xiaolei Huang

Large language models (LLMs), such as ChatGPT, have received substantial attention due to their capabilities for understanding and generating human language. While there has been a burgeoning trend in research focusing on the employment of…

The development of large language models (LLMs) such as ChatGPT has brought a lot of attention recently. However, their evaluation in the benchmark academic datasets remains under-explored due to the difficulty of evaluating the generative…

The rise of Large Language Models (LLMs) has revolutionized natural language processing across numerous languages and tasks. However, evaluating LLM performance in a consistent and meaningful way across multiple European languages remains…

Large Language Models (LLMs) have demonstrated remarkable instruction-following capabilities across various applications. However, their performance in multilingual settings lacks systematic investigation, with existing evaluations lacking…

计算与语言 · 计算机科学 2025-11-04 Zhenyu Li , Kehai Chen , Yunfei Long , Xuefeng Bai , Yaoyin Zhang , Xuchen Wei , Juntao Li , Min Zhang

Recent advancement in large language models (LLMs) has offered a strong potential for natural language systems to process informal language. A representative form of informal language is slang, used commonly in daily conversations and…

计算与语言 · 计算机科学 2024-04-16 Zhewei Sun , Qian Hu , Rahul Gupta , Richard Zemel , Yang Xu

Large language models (LLMs) have demonstrated impressive performance across a wide range of Natural Language Processing (NLP) tasks. However, ensuring their effectiveness across multiple languages presents unique challenges. Multilingual…

计算与语言 · 计算机科学 2025-05-20 Shubham Vatsal , Harsh Dubey , Aditi Singh

Recently, large pre-trained language models, such as BERT, have reached state-of-the-art performance in many natural language processing tasks, but for many languages, including Estonian, BERT models are not yet available. However, there…

计算与语言 · 计算机科学 2021-01-11 Claudia Kittask , Kirill Milintsevich , Kairit Sirts

Large Language Models (LLMs) have demonstrated remarkable capabilities in important tasks such as natural language understanding and language generation, and thus have the potential to make a substantial impact on our society. Such…

计算与语言 · 计算机科学 2024-05-24 Zhongwei Wan , Xin Wang , Che Liu , Samiul Alam , Yu Zheng , Jiachen Liu , Zhongnan Qu , Shen Yan , Yi Zhu , Quanlu Zhang , Mosharaf Chowdhury , Mi Zhang

Recent studies show that large language models (LLMs) are powerful tools for working with natural language, bringing advances in many areas of computational linguistics. However, these models face challenges when applied to low-resource…

计算与语言 · 计算机科学 2024-12-09 Zhaojun Ding , Zhengliang Liu , Hanqi Jiang , Yizhu Gao , Xiaoming Zhai , Tianming Liu , Ninghao Liu

Large language models (LLMs) have demonstrated multilingual capabilities, yet they are mostly English-centric due to the imbalanced training corpora. While prior works have leveraged this bias to enhance multilingual performance through…

计算与语言 · 计算机科学 2025-04-22 Chaoqun Liu , Wenxuan Zhang , Yiran Zhao , Anh Tuan Luu , Lidong Bing

Large Language Models (LLMs) demonstrate impressive general knowledge and reasoning abilities, yet their evaluation has predominantly focused on global or anglocentric subjects, often neglecting low-resource languages and culturally…

Despite the widespread availability of LLMs, there remains a substantial gap in their capabilities and availability across diverse languages. One approach to address these issues has been to take an existing pre-trained LLM and continue to…

计算与语言 · 计算机科学 2024-07-19 Zoltan Csaki , Bo Li , Jonathan Li , Qiantong Xu , Pian Pawakapan , Leon Zhang , Yun Du , Hengyu Zhao , Changran Hu , Urmish Thakker

Multimodal large language models (MLLMs) have proven effective in a wide range of tasks requiring complex reasoning and linguistic comprehension. However, due to a lack of high-quality multimodal resources in languages other than English,…

While large language models (LLMs) like ChatGPT have shown impressive capabilities in Natural Language Processing (NLP) tasks, a systematic investigation of their potential in this field remains largely unexplored. This study aims to…

计算与语言 · 计算机科学 2025-08-26 Libo Qin , Qiguang Chen , Xiachong Feng , Yang Wu , Yongheng Zhang , Yinghui Li , Min Li , Wanxiang Che , Philip S. Yu