Data Advisor: Dynamic Data Curation for Safety Alignment of Large
  Language Models
Authors: name: Fei Wang, name: Ninareh Mehrabi, name: Palash Goyal, name: Rahul Gupta, name: Kai-Wei Chang, name: Aram Galstyan

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.