Summary of Improving Data Efficiency Via Curating Llm-driven Rating Systems, by Jinlong Pang et al.
Improving Data Efficiency via Curating LLM-Driven Rating Systems
by Jinlong Pang, Jiaheng Wei, Ankit Parag Shah, Zhaowei Zhu, Yaxuan Wang, Chen Qian, Yang Liu, Yujia Bao, Wei Wei
First submitted to arxiv on: 9 Oct 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 presents a novel approach to adapting large language models (LLMs) for downstream tasks. The authors show that small amounts of human-curated data can outperform larger datasets, challenging traditional data scaling laws. They introduce DS2, a method for selecting diverse and accurate data samples using LLM-based scores. The approach is tested on various machine-alignment benchmarks and achieves similar or better results than full-scale datasets with the same sample size. This work highlights the importance of diversity in data selection and challenges conventional assumptions about data scaling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make computers learn from information. It shows that giving them a little bit of good-quality information can be much better than giving them a lot of poor-quality information. The authors also introduce a new way to pick the most useful information, called DS2. This method uses computer models to choose the best data and makes sure it is diverse, so computers don’t learn the same thing multiple times. The results are impressive, showing that just 3% of the original dataset can be as good or better than using all the data. |
Keywords
» Artificial intelligence » Alignment » Scaling laws
