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Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most…

Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many open-source LLMs have been released with technical reports,…

Various large language models (LLMs) have been proposed in recent years, including closed- and open-source ones, continually setting new records on multiple benchmarks. However, the development of LLMs still faces several issues, such as…

Computation and Language · Computer Science 2024-04-05 Ruyi Gan , Ziwei Wu , Renliang Sun , Junyu Lu , Xiaojun Wu , Dixiang Zhang , Kunhao Pan , Junqing He , Yuanhe Tian , Ping Yang , Qi Yang , Hao Wang , Jiaxing Zhang , Yan Song

Large language models (LLMs), with their powerful generative capabilities and vast knowledge, empower various tasks in everyday life. However, these abilities are primarily concentrated in high-resource languages, leaving low-resource…

Computation and Language · Computer Science 2024-12-20 Shaolei Zhang , Kehao Zhang , Qingkai Fang , Shoutao Guo , Yan Zhou , Xiaodong Liu , Yang Feng

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…

Large Language Models (LLMs) have demonstrated remarkable performance across various natural language tasks, marking significant strides towards general artificial intelligence. While general artificial intelligence is leveraged by…

Computation and Language · Computer Science 2023-10-31 Yizhe Yang , Huashan Sun , Jiawei Li , Runheng Liu , Yinghao Li , Yuhang Liu , Heyan Huang , Yang Gao

Recent breakthroughs in large language models (LLMs) have centered around a handful of data-rich languages. What does it take to broaden access to breakthroughs beyond first-class citizen languages? Our work introduces Aya, a massively…

Large Language Models (LLMs) achieve impressive performance in a wide range of tasks, even if they are often trained with the only objective of chatting fluently with users. Among other skills, LLMs show emergent abilities in mathematical…

Computation and Language · Computer Science 2024-06-12 Flavio Petruzzellis , Alberto Testolin , Alessandro Sperduti

We introduce the Yi model family, a series of language and multimodal models that demonstrate strong multi-dimensional capabilities. The Yi model family is based on 6B and 34B pretrained language models, then we extend them to chat models,…

The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI). However, replicating such advancements in open-source models has been challenging. This…

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,…

We release and introduce the TigerBot family of large language models (LLMs), consisting of base and chat models, sized from 7, 13, 70 and 180 billion parameters. We develop our models embarking from Llama-2 and BLOOM, and push the boundary…

Computation and Language · Computer Science 2023-12-18 Ye Chen , Wei Cai , Liangmin Wu , Xiaowei Li , Zhanxuan Xin , Cong Fu

Multilingual Large Language Models (MLLMs) represent a pivotal advancement in democratizing artificial intelligence across linguistic boundaries. While theoretical foundations are well-established, practical implementation guidelines remain…

Computation and Language · Computer Science 2024-10-24 Junhua Liu , Bin Fu

In this paper, we propose and describe the first open Llama2 large language models (LLMs) for the Lithuanian language, including an accompanying question/answer (Q/A) dataset and translations of popular LLM benchmarks. We provide a brief…

Computation and Language · Computer Science 2025-05-01 Artūras Nakvosas , Povilas Daniušis , Vytas Mulevičius

Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach,…

Large language models have emerged as a promising approach towards achieving general-purpose AI agents. The thriving open-source LLM community has greatly accelerated the development of agents that support human-machine dialogue interaction…

Computer Vision and Pattern Recognition · Computer Science 2023-11-07 Zhenfei Yin , Jiong Wang , Jianjian Cao , Zhelun Shi , Dingning Liu , Mukai Li , Lu Sheng , Lei Bai , Xiaoshui Huang , Zhiyong Wang , Jing Shao , Wanli Ouyang

Large language models (LLMs) have exhibited impressive capabilities across a myriad of tasks, yet they occasionally yield undesirable outputs. We posit that these limitations are rooted in the foundational autoregressive architecture of…

Computation and Language · Computer Science 2025-03-03 Cheng Yang , Chufan Shi , Siheng Li , Bo Shui , Yujiu Yang , Wai Lam

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…

Computation and Language · Computer Science 2024-07-19 Zoltan Csaki , Bo Li , Jonathan Li , Qiantong Xu , Pian Pawakapan , Leon Zhang , Yun Du , Hengyu Zhao , Changran Hu , Urmish Thakker

Large Language Models (LLMs) have demonstrated surprising performance across various natural language processing tasks. Recently, medical LLMs enhanced with domain-specific knowledge have exhibited excellent capabilities in medical…

Computation and Language · Computer Science 2024-09-24 Jinqiang Wang , Huansheng Ning , Yi Peng , Qikai Wei , Daniel Tesfai , Wenwei Mao , Tao Zhu , Runhe Huang
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