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相关论文: SeaLLMs -- Large Language Models for Southeast Asi…

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Sailor2 is a family of cutting-edge multilingual language models for South-East Asian (SEA) languages, available in 1B, 8B, and 20B sizes to suit diverse applications. Building on Qwen2.5, Sailor2 undergoes continuous pre-training on 500B…

Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions. However, the development of LLMs has been primarily focused on high-resource languages, such as English,…

Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The recent progress in large-scale generative models has further expanded their use in real-world language applications. However, the…

计算与语言 · 计算机科学 2024-04-12 Linyi Yang , Shuibai Zhang , Zhuohao Yu , Guangsheng Bao , Yidong Wang , Jindong Wang , Ruochen Xu , Wei Ye , Xing Xie , Weizhu Chen , Yue Zhang

Large language models (LLMs) have achieved strong results in mathematical reasoning, and are increasingly deployed as tutoring and learning support tools in educational settings. However, their reliability for students working in…

计算与语言 · 计算机科学 2026-04-20 Sukumar Kishanthan , Kumar Thushalika , Buddhi Jayasekara , Asela Hevapathige

Large Language Models (LLMs) have shown significant advances in the past year. In addition to new versions of GPT and Llama, several other LLMs have been introduced recently. Some of these are open models available for download and…

计算与语言 · 计算机科学 2024-08-01 Ravindu Jayakody , Gihan Dias

Large language models (LLMs) have showcased profound capabilities in language understanding and generation, facilitating a wide array of applications. However, there is a notable paucity of detailed, open-sourced methodologies on…

While code-mixing is a common linguistic practice in many parts of the world, collecting high-quality and low-cost code-mixed data remains a challenge for natural language processing (NLP) research. The recent proliferation of Large…

Large language models (LLMs) have demonstrated remarkable potential in handling multilingual machine translation (MMT). In this paper, we systematically investigate the advantages and challenges of LLMs for MMT by answering two questions:…

计算与语言 · 计算机科学 2024-06-17 Wenhao Zhu , Hongyi Liu , Qingxiu Dong , Jingjing Xu , Shujian Huang , Lingpeng Kong , Jiajun Chen , Lei Li

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

Large Language Models (LLMs) demonstrate remarkable translation capabilities in high-resource language tasks, yet their performance in low-resource languages is hindered by insufficient multilingual data during pre-training. To address…

计算与语言 · 计算机科学 2024-10-15 Yinquan Lu , Wenhao Zhu , Lei Li , Yu Qiao , Fei Yuan

Based on the foundation of Large Language Models (LLMs), Multilingual LLMs (MLLMs) have been developed to address the challenges faced in multilingual natural language processing, hoping to achieve knowledge transfer from high-resource…

计算与语言 · 计算机科学 2024-12-10 Yuemei Xu , Ling Hu , Jiayi Zhao , Zihan Qiu , Kexin XU , Yuqi Ye , Hanwen Gu

Large language models (LLMs) have demonstrated emergent abilities in text generation, question answering, and reasoning, facilitating various tasks and domains. Despite their proficiency in various tasks, LLMs like PaLM 540B and Llama-3.1…

The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing, attracting global attention in both academia and industry. To mitigate potential discrimination and…

Large language models (LLMs) are powerful but static; they lack mechanisms to adapt their weights in response to new tasks, knowledge, or examples. We introduce Self-Adapting LLMs (SEAL), a framework that enables LLMs to self-adapt by…

机器学习 · 计算机科学 2025-09-19 Adam Zweiger , Jyothish Pari , Han Guo , Ekin Akyürek , Yoon Kim , Pulkit Agrawal

Large language models (LLMs) with different architectures and sizes have been developed. Serving each LLM with dedicated GPUs leads to resource waste and service inefficiency due to the varying demand of LLM requests. A common practice is…

分布式、并行与集群计算 · 计算机科学 2025-04-23 Yihao Zhao , Jiadun Chen , Peng Sun , Lei Li , Xuanzhe Liu , Xin Jin

Large Language Models (LLMs) deliver powerful AI capabilities but face deployment challenges due to high resource costs and latency, whereas Small Language Models (SLMs) offer efficiency and deployability at the cost of reduced performance.…

人工智能 · 计算机科学 2025-05-13 Yi Chen , JiaHao Zhao , HaoHao Han

In recent years, large language models (e.g., Open AI's GPT-4, Meta's LLaMa, Google's PaLM) have become the dominant approach for building AI systems to analyze and generate language online. However, the automated systems that increasingly…

计算与语言 · 计算机科学 2023-06-14 Gabriel Nicholas , Aliya Bhatia

Large Language Models (LLMs) represent a class of deep learning models adept at understanding natural language and generating coherent responses to various prompts or queries. These models far exceed the complexity of conventional neural…

机器学习 · 计算机科学 2024-12-05 Minghao Shao , Abdul Basit , Ramesh Karri , Muhammad Shafique

Large language models (LLMs) are increasingly being adopted in educational settings. These applications expand beyond English, though current LLMs remain primarily English-centric. In this work, we ascertain if their use in education…

计算与语言 · 计算机科学 2025-08-06 Vansh Gupta , Sankalan Pal Chowdhury , Vilém Zouhar , Donya Rooein , Mrinmaya Sachan

Existing large language models show disparate capability across different languages, due to the imbalance in the training data. Their performances on English tasks are often stronger than on tasks of other languages. In this paper, we…

计算与语言 · 计算机科学 2023-10-10 Wenhao Zhu , Yunzhe Lv , Qingxiu Dong , Fei Yuan , Jingjing Xu , Shujian Huang , Lingpeng Kong , Jiajun Chen , Lei Li