Summary of Tele-flm Technical Report, by Xiang Li et al.
Tele-FLM Technical Report
by Xiang Li, Yiqun Yao, Xin Jiang, Xuezhi Fang, Chao Wang, Xinzhang Liu, Zihan Wang, Yu Zhao, Xin Wang, Yuyao Huang, Shuangyong Song, Yongxiang Li, Zheng Zhang, Bo Zhao, Aixin Sun, Yequan Wang, Zhongjiang He, Zhongyuan Wang, Xuelong Li, Tiejun Huang
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
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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 This paper introduces Tele-FLM (aka FLM-2), a 52-billion-parameter multilingual large language model that leverages an efficient pre-training paradigm and enhanced factual judgment capabilities. The authors aim to address the lack of open-sourced methodologies for scaling LLMs beyond 50 billion parameters while minimizing trial-and-error costs and computational resources. Tele-FLM demonstrates superior multilingual language modeling abilities, outperforming strong open-sourced models like Llama2-70B and DeepSeek-67B on benchmark tasks. The authors share the model weights, core designs, engineering practices, and training details to benefit both academic and industrial communities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a big step forward in developing large language models that can understand and generate many languages. The researchers created a new kind of model called Tele-FLM that is very good at understanding and generating text in different languages. They also shared the secrets behind how they trained this model, so others can use it to improve their own language-understanding abilities. |
Keywords
» Artificial intelligence » Language understanding » Large language model
