Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data
  Assessment and Selection for Instruction Tuning of Language Models
Authors: name: Yulei Qin, name: Yuncheng Yang, name: Pengcheng Guo, name: Gang Li, name: Hang Shao, name: Yuchen Shi, name: Zihan Xu, name: Yun Gu, name: Ke Li, name: Xing Sun

Instruction tuning plays a critical role in aligning large language models
(LLMs) with human preference. Despite the vast amount of open instruction
datasets, naively training a LLM on all existing instructions may not be
optimal and practical. To pinpoint the most beneficial datapoints, data
assessment and selection methods have been proposed in the fields of natural
language processing (NLP) and deep learning. However, under the context of
instruction tuning, there still exists a gap in knowledge on what kind of data
evaluation metrics can be employed and how they can be integrated into the
selection mechanism. To bridge this gap, we present a comprehensive review on
existing literature of data assessment and selection especially for instruction
tuning of LLMs. We systematically categorize all applicable methods into
quality-based, diversity-based, and importance-based ones where a unified,
fine-grained taxonomy is structured. For each category, representative methods
are elaborated to describe the landscape of relevant research. In addition,
comparison between latest methods is conducted on their officially reported
results to provide in-depth discussions on their limitations. Finally, we
summarize the open challenges and propose the promosing avenues for future
studies. All related contents are available at
https://github.com/yuleiqin/fantastic-data-engineering.