Data Advisor: Dynamic Data Curation for Safety Alignment of Large
  Language Models
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.
67098b78af866839069a30df
Fei Wang
extracted_confirmed
2024-10-11T20:48:23.307Z
652d9db6442fb6963b778295
/avatars/318334e5ddae8fde0cbd06a1b9583ed8.svg
Fei Wang
user
fwnlp
67098b78af866839069a30e0
Ninareh Mehrabi
admin_assigned
2024-10-14T12:40:46.048Z
615623d6f838ff843bec3c47
/avatars/0ce25e87a145d77f563f97e4a7941f70.svg
Ninareh 
user
ninaM
67098b78af866839069a30e1
Palash Goyal
admin_assigned
2024-10-14T12:40:52.090Z
61cfa171fab36112e892385e
/avatars/6110ed2f41733c8ebb17abc7d934479a.svg
Palash Goyal
user
palashgo
67098b78af866839069a30e2
Rahul Gupta
67098b78af866839069a30e3
Kai-Wei Chang
admin_assigned
2024-10-14T12:41:02.761Z
60b7b9d71b90c5d07c23fbd0
https://cdn-avatars.huggingface.co/v1/production/uploads/1622653364258-noauth.jpeg
Kai-Wei Chang
user
kaiweichang
67098b78af866839069a30e4
Aram Galstyan
Data Advisor: Dynamic Data Curation for Safety Alignment of Large
  Language Models
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.
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.
large language model LLM data generated dataset Data Advisor pre-defined principles data generation safety alignment Mistral Llama2 Falcon