Abstract
Conversational large language models (LLMs) such as ChatGPT and GPT-4 have recently exhibited remarkable capabilities across various domains, capturing widespread attention from the public. To facilitate this line of research, in this paper, we report the development of MOSS, an open-sourced conversational LLM that contains 16 B parameters and can perform a variety of instructions in multi-turn interactions with humans. The base model of MOSS is pre-trained on large-scale unlabeled English, Chinese, and code data. To optimize the model for dialogue, we generate 1.1 M synthetic conversations based on user prompts collected through our earlier versions of the model API. We then perform preference-aware training on preference data annotated from AI feedback. Evaluation results on real-world use cases and academic benchmarks demonstrate the effectiveness of the proposed approaches. In addition, we present an effective practice to augment MOSS with several external tools. Through the development of MOSS, we have established a complete technical roadmap for large language models from pre-training, supervised fine-tuning to alignment, verifying the feasibility of chatGPT under resource-limited conditions and providing a reference for both the academic and industrial communities. Model weights and code are publicly available at https://github.com/OpenMOSS/MOSS.
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17 September 2024
An Erratum to this paper has been published: https://doi.org/10.1007/s11633-024-1527-z
14 September 2024
An Erratum to this paper has been published: https://doi.org/10.1007/s11633-024-1527-z
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 62022027). We also extend our gratitude to the Shanghai Artificial Intelligence Laboratory, China, for providing the computational resources.
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Tianxiang Sun received the B. Eng. degree in software engineering from Xidian University, China in 2019. He is currently a Ph.D. degree candidate in School of Computer Science, Fudan University, China.
His research interests include natural language processing and deep learning.
Xiaotian Zhang received the B. Eng. degree in civil engineering from Tongji University, China in 2021. He received the M. Eng. degree in computer science and technology at Fudan University, China in 2004, under the supervision of Professor Xipeng Qiu.
His research interest is natural language processing.
Zhengfu He received the B. Sc. degree in computer science from Fudan University, China in 2023. He is a Ph.D. degree candidate at Fudan University, China, supervised by Professor Xipeng Qiu.
His research interests include mechanistic interpretability and large language models.
Peng Li received the B. Eng. degree in data science from East China Normal University, China in 2020. He is now a master student at Fudan University, China, supervised by Professor Xipeng Qiu.
His research interest is foundation models.
Qinyuan Cheng received the B. Eng. degree in computer science from Sun Yat-Sen University, China in 2020. He is a Ph.D. degree candidate at Fudan University, China, supervised by Professor Xipeng Qiu.
His research interest is large language models.
Xiangyang Liu received the B. Eng. degree in intelligence science and technology from Xidian University, China in 2020. He is now a Ph. D. degree candidate at Fudan University, China, supervised by Professor Xipeng Qiu.
His research interests include language model training, efficient methods and AI alignment.
Hang Yan received the B. Eng. degree in electrical engineering and automation from Fudan University, China in 2015, received the M. Eng. degree in electrical engineer at Columbia University, USA in 2017. He is a Ph.D. degree candidate in computer science from Fudan University, China, under the supervision of Professor XiPeng Qiu.
His research interests include large model training, information extraction, and open-source software development.
Yunfan Shao received the B. Sc. and M. Sc. degrees in computer science from Fudan University, China in 2019 and 2022, respectively. He is a Ph.D. degree candidate at Fudan University, China.
His research interest is large language models.
Qiong Tang received the B. Sc. degree in data science from East China Normal University, China in 2022. She is a master student at Fudan University, China, supervised by Professor Xipeng Qiu.
His research interest is large language models.
Shiduo Zhang received the B. Eng. degree in software engineering from Tongji University, China in 2023. He is now a master student at Fudan University, China, supervised by Professor Xipeng Qiu.
His research interests include foundation models and embodied AI.
Xingjian Zhao received the B. Sc. degree in artificial intelligence from Fudan University, China in 2024. He is now a master student in computer science at Fudan University, China.
His research interest is large language models.
Ke Chen is an open source contributor for open-moss project and moss backend, interested in system software. He is now pursuing the Bachelor’s degree in computer science at Fudan University, China.
His research interests include natural language processing and artificial intelligence
Yining Zheng received the B. Sc. degree in computer science from Fudan University, China in 2019. He is now a Ph.D. degree candidate at Fudan University, China, supervised by Professor Xipeng Qiu.
His research interests include large language model training and efficient methods.
Zhejian Zhou received the B. Sc. degree in electronic and information science and technology from the School of Electronics Engineering and Computer Science, Peking University, China. He was a visiting student at the Fudan NLP Group. He is currently a Ph.D. degree candidate in computer science at University of Southern California, USA.
His research interests include artificial intelligence and natural language processing.
Ruixiao Li received the B. Sc. degree in computer science from Fudan University, China in 2024. He is now a Ph.D. degree candidate in computer science at Fudan University, China.
His research interest is large language models.
Jun Zhan received the B. Eng. degree in software engineering from Huazhong University of Science and Technology, China in 2022, and is currently a master student computer science at Fudan University, China.
His research interest is large language models.
Yunhua Zhou received the M. Sc. and Ph.D. degrees in computer science from Fudan University, China in 2019 and 2024, respectively. Currently, He is a researcher at the Shanghai Artificial Intelligence Laboratory, China.
His research interest is large language models.
Linyang Li received the B. Eng. degree in electronical engineering from Fudan University, China in 2019. He is a Ph.D. degree candidate in computer science from Fudan University, China, under the supervision of Professor Xipeng Qiu.
His research interests include large model training, AI safety studies on large language models.
Xiaogui Yang received the B. Sc. and M. Eng. degrees in computer science from Fudan University, China in 2021 and 2024, respectively. Currently, he is an engineer at the Shanghai Artificial Intelligence Laboratory, China.
His research interest is large language models.
Lingling Wu received the B. Sc. degree in computer science from Shanghai JiaoTong University and M. Eng. degree in computer science from Fudan University, China in 2021 and 2024, respectively.
Her research interest is natural language processing.
Zhangyue Yin received the B. Sc. degree in data science from East China Normal University, China in 2021. He is now a Ph.D. degree candidate at Fudan University, China, supervised by Professor Xipeng Qiu and Professor Xuanjing Huang.
His research interests include large language models and machine reasoning.
Xuanjing Huang received the Ph.D. degree in computer science from Fudan University, China in 1998. She is currently a professor at the School of Computer Science, Fudan University, China.
Her research interests include natural language processing and information retrieval, with a particular emphasis on sentiment analysis, information extraction, pre-trained language models, and the robustness and interpretability of NLP.
Yu-Gang Jiang received the Ph.D. degree in computer science from City University of Hong Kong, China in 2009. He is Vice President of Fudan University, China, and a Chang Jiang Scholar Distinguished Professor of Computer Science. He is a Fellow of IEEE and IAPR.
His research interests include multimedia, computer vision, and trustworthy AGI.
Xipeng Qiu received the B. Sc. degree and Ph.D. degrees in computer science from Fudan University, China in 2001 and 2006, respectively. Currently, he is a professor at School of Computer Science, Fudan University, China.
His research interests include natural language processing and deep learning.
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The original version of this article was revised due to a retrospective Open Access order
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Sun, T., Zhang, X., He, Z. et al. MOSS: An Open Conversational Large Language Model. Mach. Intell. Res. 21, 888–905 (2024). https://doi.org/10.1007/s11633-024-1502-8
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DOI: https://doi.org/10.1007/s11633-024-1502-8