Data Advisor: Dynamic Data Curation for Safety Alignment of Large Language Models (2410.05269v1)

Abstract: Data is a crucial element in 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.

• The paper introduces a dynamic curation mechanism that iteratively improves LLM data quality for safety alignment.
• It employs a three-step process—data summarization, weakness identification, and corrective generation—to enhance safety scores without reducing utility.
• Experimental results show significant improvements, including a +10.1 safety score increase on CatQA and balanced performance across diverse safety categories.

Dynamic Data Curation for Safety Alignment of LLMs

The paper "Dynamic Data Curation for Safety Alignment of LLMs" addresses a key challenge in the deployment of LLMs: aligning their output to be both safe and useful. The authors propose a novel method, named , which dynamically enhances data curation processes to improve the alignment of LLMs with safety guidelines without compromising their utility.

Overview

The motivation behind this work stems from the challenges associated with using LLMs for data generation. Although LLMs can generate large datasets, these often suffer from quality deficiencies, such as lack of diversity and the presence of biases. A significant problem is the inattention to dataset-level properties that can lead to underrepresentation of crucial aspects, such as diverse safety concerns.

introduces a mechanism to guide the data generation process dynamically. It leverages predefined principles to monitor the data's quality iteratively, identifying weaknesses and advising improvements in subsequent iterations. This method is shown to be effective when applied to safety align Mistral, Llama2, and Falcon models, enhancing their safety features while maintaining their general utility.

Methodology

employs a structured three-step process:

1. Data Summarization: The advisor generates a concise report of current data properties based on previous summaries and new instances. This step ensures that the model's data comprehensively reflects the target dataset's guiding principles.
2. Weakness Identification: It identifies underrepresented aspects in the data, focusing on missing or inadequately covered safety issues. This targeted assessment aids in strategically improving dataset quality.
3. Data Generation with Advice: Guided by identified weaknesses, this phase facilitates generating new data that addresses specific shortcomings. By doing so, it seamlessly integrates control signals into data generation procedures.

Experimental Results

The paper presents robust experimental validation, illustrating that data generated by consistently outperforms other methods such as Self-Instruct. Across three base LLMs, safety and utility scores show marked improvement. For example, achieves a +10.1 increase in safety scores on the CatQA dataset and a +4.6 increase on BeaverTails, indicating its superior handling of safety issues without degrading utility scores.

Analyses

The paper provides comprehensive analyses:

• Fine-Grained Safety: shows improvements across all safety categories, reducing harmful responses notably in areas like Economic Harm and Violence.
• Data Diversity: The method achieves higher n-gram diversity compared to Self-Instruct, highlighting the improved diversity in the generated data.
• Data Mixture: A balanced combination of safety alignment and instruction tuning data is demonstrated to be crucial in achieving optimal model performance in both safety and utility.
• Qualitative Insights: Examples of generated data illustrate 's capability in iteratively identifying diverse safety issues.

Implications and Future Directions

implies significant advancements for the practical deployment of safer LLMs. By dynamically updating datasets with better coverage of safety issues, it can serve a broader range of LLM applications. The proactive data generation it proposes can be leveraged to optimize other facets of AI training, such as bias reduction in preference optimization or tailored task adaptation.

While the method focuses primarily on safety alignment, its potential for broader applications remains largely unexplored. Future work could investigate extending this framework to different scenarios, including domain adaptation and addressing biases across various AI applications. As such, offers a promising approach for evolving dynamic and adaptive LLM training methodologies.