title: Data Advisor: Dynamic Data Curation for Safety Alignment of Large
    Language Models
summary: Data is a crucial element in large language model (LLM) alignment. Recent
  studies have explored using LLMs for efficient data collection. However,
  LLM-generated data often suffers from quality issues, with underrepresented or
  absent aspects and low-quality datapoints. To address these problems, we
  propose Data Advisor, an enhanced LLM-based method for generating data that
  takes into account the characteristics of the desired dataset. Starting from a
  set of pre-defined principles in hand, Data Advisor monitors the status of the
  generated data, identifies weaknesses in the current dataset, and advises the
  next iteration of data generation accordingly. Data Advisor can be easily
  integrated into existing data generation methods to enhance data quality and
  coverage. Experiments on safety alignment of three representative LLMs (i.e.,
  Mistral, Llama2, and Falcon) demonstrate the effectiveness of Data Advisor in
  enhancing model safety against various fine-grained safety issues without
  sacrificing model utility.
paper_authors._id: 67098b78af866839069a30df
paper_authors.name: Fei Wang
paper_authors.status: extracted_confirmed
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paper_authors.avatarUrl: /avatars/318334e5ddae8fde0cbd06a1b9583ed8.svg
paper_authors.fullname: Fei Wang
paper_authors.type: user
paper_authors.user: fwnlp
paper_authors._id: 67098b78af866839069a30e0
paper_authors.name: Ninareh Mehrabi
paper_authors.status: admin_assigned
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paper_authors.fullname: Ninareh 
paper_authors.type: user
paper_authors.user: ninaM
paper_authors._id: 67098b78af866839069a30e1
paper_authors.name: Palash Goyal
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-14T12:40:52.090Z
paper_authors._id: 61cfa171fab36112e892385e
paper_authors.avatarUrl: /avatars/6110ed2f41733c8ebb17abc7d934479a.svg
paper_authors.fullname: Palash Goyal
paper_authors.type: user
paper_authors.user: palashgo
paper_authors._id: 67098b78af866839069a30e2
paper_authors.name: Rahul Gupta
paper_authors._id: 67098b78af866839069a30e3
paper_authors.name: Kai-Wei Chang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-14T12:41:02.761Z
paper_authors._id: 60b7b9d71b90c5d07c23fbd0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1622653364258-noauth.jpeg
paper_authors.fullname: Kai-Wei Chang
paper_authors.type: user
paper_authors.user: kaiweichang
paper_authors._id: 67098b78af866839069a30e4
paper_authors.name: Aram Galstyan
paper_title: Data Advisor: Dynamic Data Curation for Safety Alignment of Large
    Language Models
paper_summary: Data is a crucial element in large language model (LLM) alignment. Recent
  studies have explored using LLMs for efficient data collection. However,
  LLM-generated data often suffers from quality issues, with underrepresented or
  absent aspects and low-quality datapoints. To address these problems, we
  propose Data Advisor, an enhanced LLM-based method for generating data that
  takes into account the characteristics of the desired dataset. Starting from a
  set of pre-defined principles in hand, Data Advisor monitors the status of the
  generated data, identifies weaknesses in the current dataset, and advises the
  next iteration of data generation accordingly. Data Advisor can be easily
  integrated into existing data generation methods to enhance data quality and
  coverage. Experiments on safety alignment of three representative LLMs (i.e.,
  Mistral, Llama2, and Falcon) demonstrate the effectiveness of Data Advisor in
  enhancing model safety against various fine-grained safety issues without
  sacrificing model utility.
paper_ai_summary: Data Advisor is an enhanced method for generating high-quality and comprehensive dataset using LLMs, improving model safety across various fine-grained issues without compromising utility.
paper_ai_keywords: large language model LLM data generated dataset Data Advisor pre-defined principles data generation safety alignment Mistral Llama2 Falcon