UCSC-REAL/DS2
[ICLR 2025] Official implementation of paper "Improving Data Efficiency via Curating LLM-Driven Rating Systems"
DS2 helps researchers and engineers improve the quality of datasets used to train large language models (LLMs). It takes raw, LLM-generated quality scores for data samples, corrects common errors in these scores, and then curates a final dataset that is both high-quality and diverse. This results in more efficient and effective LLM instruction tuning.
101 stars. No commits in the last 6 months.
Use this if you are an AI researcher or machine learning engineer struggling with noisy or inconsistent quality ratings when preparing training data for your LLMs.
Not ideal if you need a tool for general data cleaning or a system for human-in-the-loop data labeling.
Stars
101
Forks
9
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 24, 2025
Commits (30d)
0
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