Summary of Dataverse: Open-source Etl (extract, Transform, Load) Pipeline For Large Language Models, by Hyunbyung Park et al.
Dataverse: Open-Source ETL (Extract, Transform, Load) Pipeline for Large Language Models
by Hyunbyung Park, Sukyung Lee, Gyoungjin Gim, Yungi Kim, Dahyun Kim, Chanjun Park
First submitted to arxiv on: 28 Mar 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 This paper proposes Dataverse, an open-source Extract-Transform-Load (ETL) pipeline designed for large language models (LLMs). The pipeline’s user-friendly interface allows users to easily build their own ETL pipelines by adding custom processors. The authors hope that Dataverse will become a valuable tool for LLM development and are open-sourcing the entire library to encourage community contributions. To illustrate its capabilities, the paper includes a two-minute video demonstration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for people to use big language models by creating a special pipeline called Dataverse. This pipeline helps get data ready for use with these large models. It’s designed to be easy to use and understand, so anyone can build their own pipeline by adding extra steps as needed. The authors want to share this tool with the community and are making it open-source. They also made a short video to show how Dataverse works. |
