Summary of Telechat Technical Report, by Zhongjiang He et al.
TeleChat Technical Report
by Zhongjiang He, Zihan Wang, Xinzhang Liu, Shixuan Liu, Yitong Yao, Yuyao Huang, Xuelong Li, Yongxiang Li, Zhonghao Che, Zhaoxi Zhang, Yan Wang, Xin Wang, Luwen Pu, Huinan Xu, Ruiyu Fang, Yu Zhao, Jie Zhang, Xiaomeng Huang, Zhilong Lu, Jiaxin Peng, Wenjun Zheng, Shiquan Wang, Bingkai Yang, Xuewei he, Zhuoru Jiang, Qiyi Xie, Yanhan Zhang, Zhongqiu Li, Lingling Shi, Weiwei Fu, Yin Zhang, Zilu Huang, Sishi Xiong, Yuxiang Zhang, Chao Wang, Shuangyong Song
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The authors introduce TeleChat, a suite of large language models (LLMs) designed for conversational AI applications. The models are initially pretrained on a massive corpus containing texts from both English and Chinese languages, then fine-tuned to align with human preferences. Evaluations on various tasks, including language understanding, code generation, and knowledge-based question answering, demonstrate TeleChat’s competitive performance compared to other open-source models of similar size. Notably, the 7B and 12B variants achieve state-of-the-art results on several public benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TeleChat is a collection of special kinds of artificial intelligence called large language models (LLMs). These models are trained on lots of text from both English and Chinese languages to learn how to understand human language. The authors made the models better by fine-tuning them to match what humans prefer. They tested TeleChat’s skills in areas like understanding language, writing code, and answering tricky questions. The results show that TeleChat is as good or even better than other similar models on many tasks. |
Keywords
* Artificial intelligence * Fine tuning * Language understanding * Question answering
