title: Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data
    Assessment and Selection for Instruction Tuning of Language Models
summary: 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.
paper_authors._id: 66b1a447a90022f3af0d99e1
paper_authors.name: Yulei Qin
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paper_authors.fullname: Yulei Qin
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paper_authors.user: yolay
paper_authors._id: 66b1a447a90022f3af0d99e2
paper_authors.name: Yuncheng Yang
paper_authors._id: 66b1a447a90022f3af0d99e3
paper_authors.name: Pengcheng Guo
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paper_authors.fullname: Pengcheng Guo
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paper_authors.user: pcguo
paper_authors._id: 66b1a447a90022f3af0d99e4
paper_authors.name: Gang Li
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paper_authors.fullname: Gang Li
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paper_authors.user: ligang-cs
paper_authors._id: 66b1a447a90022f3af0d99e5
paper_authors.name: Hang Shao
paper_authors.status: admin_assigned
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paper_authors.fullname: shaohang
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paper_authors.user: shaohang
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paper_authors.name: Yuchen Shi
paper_authors._id: 66b1a447a90022f3af0d99e7
paper_authors.name: Zihan Xu
paper_authors._id: 66b1a447a90022f3af0d99e8
paper_authors.name: Yun Gu
paper_authors._id: 66b1a447a90022f3af0d99e9
paper_authors.name: Ke Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-09T10:11:46.065Z
paper_authors._id: 63280915eeee4dd858083092
paper_authors.avatarUrl: /avatars/78347af4af42527d53e88d9969c5c934.svg
paper_authors.fullname: Ke Li
paper_authors.type: user
paper_authors.user: tristanli
paper_authors._id: 66b1a447a90022f3af0d99ea
paper_authors.name: Xing Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-06T10:18:23.955Z
paper_authors._id: 647401e50da364bd0d002f2a
paper_authors.avatarUrl: /avatars/f1586f610f21ddb4f868856208c2cfab.svg
paper_authors.fullname: XING SUN
paper_authors.type: user
paper_authors.user: tedsun
paper_title: Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data
    Assessment and Selection for Instruction Tuning of Language Models
paper_summary: 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.
paper_ai_summary: A review of existing data assessment and selection methods for instruction tuning of large language models categorizes them into quality-based, diversity-based, and importance-based approaches, discusses their limitations, and outlines future research directions.
paper_ai_keywords: large language models instruction tuning data assessment data selection quality-based diversity-based importance-based taxonomy fine-grained