Summary of Map-neo: Highly Capable and Transparent Bilingual Large Language Model Series, by Ge Zhang et al.
MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series
by Ge Zhang, Scott Qu, Jiaheng Liu, Chenchen Zhang, Chenghua Lin, Chou Leuang Yu, Danny Pan, Esther Cheng, Jie Liu, Qunshu Lin, Raven Yuan, Tuney Zheng, Wei Pang, Xinrun Du, Yiming Liang, Yinghao Ma, Yizhi Li, Ziyang Ma, Bill Lin, Emmanouil Benetos, Huan Yang, Junting Zhou, Kaijing Ma, Minghao Liu, Morry Niu, Noah Wang, Quehry Que, Ruibo Liu, Sine Liu, Shawn Guo, Soren Gao, Wangchunshu Zhou, Xinyue Zhang, Yizhi Zhou, Yubo Wang, Yuelin Bai, Yuhan Zhang, Yuxiang Zhang, Zenith Wang, Zhenzhu Yang, Zijian Zhao, Jiajun Zhang, Wanli Ouyang, Wenhao Huang, Wenhu Chen
First submitted to arxiv on: 29 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A recent surge in Large Language Model (LLM) performance has been achieved across various tasks, with models like GPT, Gemini, and Claude leading the way. However, these competitive models are often proprietary, lacking transparency regarding their training details. In contrast, open-sourced LLMs like LLaMA-3 have become more prevalent, but still only provide model weights, leaving intermediate checkpoints, pre-training corpus, and training code undisclosed. To address this lack of transparency, the research community has begun to develop truly open LLMs, such as Pythia, Amber, and OLMo, which provide more detailed information. These transparent models have improved our understanding of their strengths, weaknesses, biases, and risks. Despite this progress, existing truly open LLMs still trail behind state-of-the-art LLMs in terms of performance on reasoning, knowledge, and coding tasks. To bridge this gap, we introduce MAP-Neo, a bilingual language model with 7B parameters trained from scratch on high-quality tokens. Our model is the first fully open-sourced bilingual LLM to match the performance of state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can do many tasks, like answer questions or write text. Right now, some of these models are only available for special companies to use, and they don’t tell us how they were trained. This makes it hard for scientists to study and improve LLMs. To fix this, researchers have started sharing their own LLMs that anyone can use. These open-source models help us learn more about what makes them strong or weak. However, these open models aren’t as good as the best ones yet. That’s why we created a new model called MAP-Neo that’s really good and completely transparent. We’re making all the details available so others can learn from it and make even better LLMs. |
Keywords
» Artificial intelligence » Claude » Gemini » Gpt » Language model » Large language model » Llama
