REVIEW 2 cited by
Dataverse: Open-Source ETL (Extract, Transform, Load) Pipeline for Large Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
To address the challenges associated with data processing at scale, we propose Dataverse, a unified open-source Extract-Transform-Load (ETL) pipeline for large language models (LLMs) with a user-friendly design at its core. Easy addition of custom processors with block-based interface in Dataverse allows users to readily and efficiently use Dataverse to build their own ETL pipeline. We hope that Dataverse will serve as a vital tool for LLM development and open source the entire library to welcome community contribution. Additionally, we provide a concise, two-minute video demonstration of our system, illustrating its capabilities and implementation.
Forward citations
Cited by 2 Pith papers
-
A Survey of LLM $\times$ DATA
A comprehensive survey of the bidirectional links between LLMs and data management, organized as DATA4LLM and LLM4DATA with a new 'IaaS' data-quality framework.
-
DBMS-LLM Integration Strategies in Industrial and Business Applications: Current Status and Future Challenges
The paper proposes a taxonomy of five DBMS-LLM integration strategies (DB-first, LLM-first, middle-layer, pipe-connected, platform-based) and outlines open challenges.
Discussion (0). Continue with ORCID to comment.