@inproceedings{wang-etal-2024-telechat,
title = "{T}ele{C}hat: An Open-source Billingual Large Language Model",
author = "Wang, Zihan and
Liu, XinZhang and
Liu, Shixuan and
Yao, Yitong and
Huang, Yunyao and
Li, Mengxiang and
He, Zhongjiang and
Li, Yongxian and
Pu, Luwen and
Xu, Huinan and
Wang, Chao and
Song, Shuangyong",
editor = "Wong, Kam-Fai and
Zhang, Min and
Xu, Ruifeng and
Li, Jing and
Wei, Zhongyu and
Gui, Lin and
Liang, Bin and
Zhao, Runcong",
booktitle = "Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.sighan-1.2/",
pages = "10--20",
abstract = "In this paper, we present \textbf{TeleChat}, a collection of large language models (LLMs) with parameters of 7 billion and 12 billion. TeleChat is initially pretrained on an extensive corpus containing a diverse collection of texts from both English and Chinese languages, encompassing trillions of tokens. Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe. We evaluate the performance of TeleChat on various tasks, including general dialogue generation, language understanding, mathematics, reasoning, code generation, and knowledge-based question answering. Our findings indicate that TeleChat achieves state-of-the-art performance to other open-source models of similar size across a wide range of public benchmarks. To support future research and applications utilizing LLMs, we release the fine-tuned model checkpoints of TeleChat-7B and TeleChat-12B, along with code and a portion of our filtered high-quality pretraining data, to the public community."
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<abstract>In this paper, we present TeleChat, a collection of large language models (LLMs) with parameters of 7 billion and 12 billion. TeleChat is initially pretrained on an extensive corpus containing a diverse collection of texts from both English and Chinese languages, encompassing trillions of tokens. Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe. We evaluate the performance of TeleChat on various tasks, including general dialogue generation, language understanding, mathematics, reasoning, code generation, and knowledge-based question answering. Our findings indicate that TeleChat achieves state-of-the-art performance to other open-source models of similar size across a wide range of public benchmarks. To support future research and applications utilizing LLMs, we release the fine-tuned model checkpoints of TeleChat-7B and TeleChat-12B, along with code and a portion of our filtered high-quality pretraining data, to the public community.</abstract>
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%0 Conference Proceedings
%T TeleChat: An Open-source Billingual Large Language Model
%A Wang, Zihan
%A Liu, XinZhang
%A Liu, Shixuan
%A Yao, Yitong
%A Huang, Yunyao
%A Li, Mengxiang
%A He, Zhongjiang
%A Li, Yongxian
%A Pu, Luwen
%A Xu, Huinan
%A Wang, Chao
%A Song, Shuangyong
%Y Wong, Kam-Fai
%Y Zhang, Min
%Y Xu, Ruifeng
%Y Li, Jing
%Y Wei, Zhongyu
%Y Gui, Lin
%Y Liang, Bin
%Y Zhao, Runcong
%S Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F wang-etal-2024-telechat
%X In this paper, we present TeleChat, a collection of large language models (LLMs) with parameters of 7 billion and 12 billion. TeleChat is initially pretrained on an extensive corpus containing a diverse collection of texts from both English and Chinese languages, encompassing trillions of tokens. Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe. We evaluate the performance of TeleChat on various tasks, including general dialogue generation, language understanding, mathematics, reasoning, code generation, and knowledge-based question answering. Our findings indicate that TeleChat achieves state-of-the-art performance to other open-source models of similar size across a wide range of public benchmarks. To support future research and applications utilizing LLMs, we release the fine-tuned model checkpoints of TeleChat-7B and TeleChat-12B, along with code and a portion of our filtered high-quality pretraining data, to the public community.
%U https://aclanthology.org/2024.sighan-1.2/
%P 10-20
Markdown (Informal)
[TeleChat: An Open-source Billingual Large Language Model](https://aclanthology.org/2024.sighan-1.2/) (Wang et al., SIGHAN 2024)
ACL
- Zihan Wang, XinZhang Liu, Shixuan Liu, Yitong Yao, Yunyao Huang, Mengxiang Li, Zhongjiang He, Yongxian Li, Luwen Pu, Huinan Xu, Chao Wang, and Shuangyong Song. 2024. TeleChat: An Open-source Billingual Large Language Model. In Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10), pages 10–20, Bangkok, Thailand. Association for Computational Linguistics.