Summary of Baichuanseed: Sharing the Potential Of Extensive Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline, By Guosheng Dong et al.
BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline
by Guosheng Dong, Da Pan, Yiding Sun, Shusen Zhang, Zheng Liang, Xin Wu, Yanjun Shen, Fan Yang, Haoze Sun, Tianpeng Li, Mingan Lin, Jianhua Xu, Yufan Zhang, Xiaonan Nie, Lei Su, Bingning Wang, Wentao Zhang, Jiaxin Mao, Zenan Zhou, Weipeng Chen
First submitted to arxiv on: 27 Aug 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 paper addresses a crucial issue in Large Language Models (LLMs), namely the reliance on extensive pretraining datasets that are often commercial secrets. To mitigate this problem, the authors open-source a universally applicable data processing pipeline and validate its effectiveness by introducing a competitive LLM baseline. The pipeline consists of broad collection to scale up and reweighting to improve quality. A 7B model, BaichuanSEED, is pre-trained using this pipeline without any deliberate downstream task-related optimization, followed by a supervised fine-tuning stage. BaichuanSEED demonstrates consistent performance on comprehensive benchmarks, comparable to commercial advanced LLMs like Qwen1.5 and Llama3. The authors also conduct heuristic experiments to explore the potential for further optimization of downstream tasks, such as mathematics and coding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making Large Language Models (LLMs) fairer by sharing a secret recipe for preparing their training data. Right now, this information is often kept private by companies that make LLMs. The authors want to change this by providing an open-source way to process data and show that it works just as well as the secret methods used by these companies. They use this method to train a large language model called BaichuanSEED and test its performance on various tasks. The results are impressive, with BaichuanSEED performing similarly to top-notch LLMs from other companies. The authors also explore ways to further improve Baichanchan’s performance. |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Optimization » Pretraining » Supervised
