Summary of Redpajama: An Open Dataset For Training Large Language Models, by Maurice Weber et al.
RedPajama: an Open Dataset for Training Large Language Models
by Maurice Weber, Daniel Fu, Quentin Anthony, Yonatan Oren, Shane Adams, Anton Alexandrov, Xiaozhong Lyu, Huu Nguyen, Xiaozhe Yao, Virginia Adams, Ben Athiwaratkun, Rahul Chalamala, Kezhen Chen, Max Ryabinin, Tri Dao, Percy Liang, Christopher Ré, Irina Rish, Ce Zhang
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The abstract discusses the importance of transparency in large language model development, particularly regarding dataset composition and filtering. The authors identify three core challenges that need to be addressed: (1) transparent model development, including data curation; (2) access to high-quality data; and (3) availability of artifacts and metadata for analysis. To address these challenges, the authors release two new datasets: RedPajama-V1, an open reproduction of the LLaMA training dataset, and RedPajama-V2, a massive web-only dataset with quality signals and metadata. The authors also present analyses and ablation studies using decoder-only language models to demonstrate the effectiveness of quality signals in curating high-quality subsets of the dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making language models more transparent and open. It talks about how some big language models were made without showing exactly what data they used, which makes it hard to make new, better models. The authors identify three main problems: not being clear about how they developed their models, not having enough good data, and not sharing the information needed to analyze the data. To fix these problems, they created two new datasets that anyone can use. One is a copy of an existing dataset, and the other is a huge collection of text from the internet with extra information that helps filter out bad data. |
Keywords
» Artificial intelligence » Decoder » Large language model » Llama
