Summary of Unleashing the Power Of Data Tsunami: a Comprehensive Survey on Data Assessment and Selection For Instruction Tuning Of Language Models, by Yulei Qin et al.
Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models
by Yulei Qin, Yuncheng Yang, Pengcheng Guo, Gang Li, Hang Shao, Yuchen Shi, Zihan Xu, Yun Gu, Ke Li, Xing Sun
First submitted to arxiv on: 4 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Signal Processing (eess.SP)
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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 Large language models (LLMs) require instruction tuning to align with human preferences. Although many open instruction datasets exist, naively training a LLM on all available data may not be optimal or practical. To address this challenge, researchers have proposed various data assessment and selection methods in natural language processing (NLP) and deep learning. However, there is a gap in understanding which evaluation metrics can be used to select the most beneficial datapoints for instruction tuning. This study presents a comprehensive review of existing literature on data assessment and selection specifically for LLM instruction tuning. We categorize applicable methods into quality-based, diversity-based, and importance-based approaches, elaborating on representative methods within each category. The landscape of relevant research is structured through this taxonomy. Additionally, we compare the latest methods based on their officially reported results to provide in-depth discussions on their limitations. Finally, we summarize open challenges and propose promising avenues for future studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models need special instructions to make them work like humans do. There are many instruction datasets available, but it’s not practical to use all of them. Researchers want to know which data points are most important for making the model better. This study looks at what other researchers have done in this area and how they decided which data was best. They found that different methods work better for different purposes. The study also talks about what still needs to be fixed and suggests new areas to explore. |
Keywords
» Artificial intelligence » Deep learning » Instruction tuning » Natural language processing » Nlp
