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相关论文: GLM-130B: An Open Bilingual Pre-trained Model

200 篇论文

Small Language Models (SLMs) enable cost-effective, on-device and latency-sensitive AI applications, yet their deployment in Traditional Chinese (TC) remains hindered by token-level instability - models unpredictably emit non-TC characters…

计算与语言 · 计算机科学 2025-10-03 Yu-Cheng Chih , Ming-Tao Duan , Yong-Hao Hou

The release of top-performing open-weight LLMs has cemented China's role as a leading force in AI development. Do these models support languages spoken in China? Or do they support the same languages as models developed in the United States…

计算与语言 · 计算机科学 2026-05-18 Andrea W Wen-Yi , Unso Eun Seo Jo , David Mimno

Large Language Models (LLMs) have seen great advance in both academia and industry, and their popularity results in numerous open-source frameworks and techniques in accelerating LLM pre-training, fine-tuning, and inference. Training and…

The success of ChatGPT validates the potential of large language models (LLMs) in artificial general intelligence (AGI). Subsequently, the release of LLMs has sparked the open-source community's interest in instruction-tuning, which is…

计算与语言 · 计算机科学 2023-10-23 Qingyi Si , Tong Wang , Zheng Lin , Xu Zhang , Yanan Cao , Weiping Wang

Large language models (LLMs) require enormous computing power to pretrain on massive datasets. When limited datasets are available, smaller-sized LLMs are better choice to pretrain (on user-specified datasets) by following the scaling laws…

机器学习 · 计算机科学 2026-03-23 Praveen Rao

Large Language Models pre-trained with self-supervised learning have demonstrated impressive zero-shot generalization capabilities on a wide spectrum of tasks. In this work, we present WeLM: a well-read pre-trained language model for…

计算与语言 · 计算机科学 2023-05-17 Hui Su , Xiao Zhou , Houjin Yu , Xiaoyu Shen , Yuwen Chen , Zilin Zhu , Yang Yu , Jie Zhou

We introduce the Falcon series: 7B, 40B, and 180B parameters causal decoder-only models trained on a diverse high-quality corpora predominantly assembled from web data. The largest model, Falcon-180B, has been trained on over 3.5 trillion…

In this technical report, we present Skywork-13B, a family of large language models (LLMs) trained on a corpus of over 3.2 trillion tokens drawn from both English and Chinese texts. This bilingual foundation model is the most extensively…

The strategic significance of Large Language Models (LLMs) in economic expansion, innovation, societal development, and national security has been increasingly recognized since the advent of ChatGPT. This study provides a comprehensive…

计算与语言 · 计算机科学 2024-05-22 Zhenhui Jiang , Jiaxin Li , Yang Liu

This project focuses on enhancing open-source large language models through instruction-tuning and providing comprehensive evaluations of their performance. We explore how various training data factors, such as quantity, quality, and…

计算与语言 · 计算机科学 2023-05-05 Fangkai Jiao , Bosheng Ding , Tianze Luo , Zhanfeng Mo

EngGPT2-16B-A3B is the latest iteration of Engineering Group's Italian LLM and it's built to be a Sovereign, Efficient and Open model. EngGPT2 is trained on 2.5 trillion tokens - less than Qwen3's 36T or Llama3's 15T - and delivers…

Pre-trained language models (PLMs) have achieved remarkable success in NLP tasks. Despite the great success, mainstream solutions largely follow the pre-training then finetuning paradigm, which brings in both high deployment costs and low…

计算与语言 · 计算机科学 2023-05-03 Xiang Li , Xin Jiang , Xuying Meng , Aixin Sun , Yequan Wang

Modern large language models (LLMs) driven by scaling laws, achieve intelligence emergency in large model sizes. Recently, the increasing concerns about cloud costs, latency, and privacy make it an urgent requirement to develop compact edge…

机器学习 · 计算机科学 2025-02-13 Xingrun Xing , Zheng Liu , Shitao Xiao , Boyan Gao , Yiming Liang , Wanpeng Zhang , Haokun Lin , Guoqi Li , Jiajun Zhang

Large Language Models (LLMs) continue to demonstrate superior performance with increasing scale, yet training models with billions to trillions of parameters requires staggering computational resources, e.g. a one-trillion-parameter…

分布式、并行与集群计算 · 计算机科学 2026-05-11 Ajay Navilarekal Rajgopal , Nikolai Solmsdorf

Pretraining auto-regressive large language models~(LLMs) with retrieval demonstrates better perplexity and factual accuracy by leveraging external databases. However, the size of existing pretrained retrieval-augmented LLM is still limited…

计算与语言 · 计算机科学 2024-05-30 Boxin Wang , Wei Ping , Lawrence McAfee , Peng Xu , Bo Li , Mohammad Shoeybi , Bryan Catanzaro

Large Language Models (LLMs), such as ChatGPT and GPT-4, have dramatically transformed natural language processing research and shown promising strides towards Artificial General Intelligence (AGI). Nonetheless, the high costs associated…

计算与语言 · 计算机科学 2024-02-26 Yiming Cui , Ziqing Yang , Xin Yao

Large language models (LLMs) have transformed natural language processing. Yet, their predominantly English-centric training has led to biases and performance disparities across languages. This imbalance marginalizes minoritized languages,…

Large language models (LLMs) have become an essential tool for natural language processing and artificial intelligence in general. Current open-source models are primarily trained on English texts, resulting in poorer performance on…

计算与语言 · 计算机科学 2026-03-03 Domen Vreš , Tjaša Arčon , Timotej Petrič , Dario Vajda , Marko Robnik-Šikonja , Iztok Lebar Bajec

Recently, significant public efforts have been directed towards developing low-cost models with capabilities akin to ChatGPT, thereby fostering the growth of open-source conversational models. However, there remains a scarcity of…

计算与语言 · 计算机科学 2023-04-18 Yunjie Ji , Yan Gong , Yong Deng , Yiping Peng , Qiang Niu , Baochang Ma , Xiangang Li

Large language models (LLMs) have shown continuously improving multilingual capabilities, and even small-scale open-source models have demonstrated rapid performance enhancement. In this paper, we systematically explore the abilities of…

计算与语言 · 计算机科学 2025-02-25 Menglong Cui , Pengzhi Gao , Wei Liu , Jian Luan , Bin Wang