Summary of Internlm2 Technical Report, by Zheng Cai et al.

InternLM2 Technical Report

by Zheng Cai, Maosong Cao, Haojiong Chen, Kai Chen, Keyu Chen, Xin Chen, Xun Chen, Zehui Chen, Zhi Chen, Pei Chu, Xiaoyi Dong, Haodong Duan, Qi Fan, Zhaoye Fei, Yang Gao, Jiaye Ge, Chenya Gu, Yuzhe Gu, Tao Gui, Aijia Guo, Qipeng Guo, Conghui He, Yingfan Hu, Ting Huang, Tao Jiang, Penglong Jiao, Zhenjiang Jin, Zhikai Lei, Jiaxing Li, Jingwen Li, Linyang Li, Shuaibin Li, Wei Li, Yining Li, Hongwei Liu, Jiangning Liu, Jiawei Hong, Kaiwen Liu, Kuikun Liu, Xiaoran Liu, Chengqi Lv, Haijun Lv, Kai Lv, Li Ma, Runyuan Ma, Zerun Ma, Wenchang Ning, Linke Ouyang, Jiantao Qiu, Yuan Qu, Fukai Shang, Yunfan Shao, Demin Song, Zifan Song, Zhihao Sui, Peng Sun, Yu Sun, Huanze Tang, Bin Wang, Guoteng Wang, Jiaqi Wang, Jiayu Wang, Rui Wang, Yudong Wang, Ziyi Wang, Xingjian Wei, Qizhen Weng, Fan Wu, Yingtong Xiong, Chao Xu, Ruiliang Xu, Hang Yan, Yirong Yan, Xiaogui Yang, Haochen Ye, Huaiyuan Ying, Jia Yu, Jing Yu, Yuhang Zang, Chuyu Zhang, Li Zhang, Pan Zhang, Peng Zhang, Ruijie Zhang, Shuo Zhang, Songyang Zhang, Wenjian Zhang, Wenwei Zhang, Xingcheng Zhang, Xinyue Zhang, Hui Zhao, Qian Zhao, Xiaomeng Zhao, Fengzhe Zhou, Zaida Zhou, Jingming Zhuo, Yicheng Zou, Xipeng Qiu, Yu Qiao, Dahua Lin

First submitted to arxiv on: 26 Mar 2024

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 research paper introduces InternLM2, an open-source Large Language Model (LLM) that surpasses its predecessors in various evaluations across 6 dimensions and 30 benchmarks. The model employs innovative pre-training and optimization techniques to excel in long-context modeling and open-ended subjective evaluations. Specifically, InternLM2 is trained on diverse data types, including text, code, and long-context data, allowing it to capture long-term dependencies efficiently. Initially trained on 4k tokens before advancing to 32k tokens, the model demonstrates remarkable performance on the “Needle-in-a-Haystack” test. The authors also propose Supervised Fine-Tuning (SFT) and Conditional Online Reinforcement Learning from Human Feedback (COOL RLHF) strategies to address conflicting human preferences and reward hacking. By releasing InternLM2 models in different training stages and model sizes, the paper provides insights into the model’s evolution.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This study creates an open-source language model called InternLM2 that does better than other similar models in many ways. The researchers used special techniques to train the model on lots of different kinds of data, which helps it understand long sentences and conversations. They also tested the model with people giving feedback, and it did a great job. The goal is to make this technology more accessible and useful for everyone.