[Paper Review] Developing Safe and Responsible Large Language Model : Can We Balance Bias Reduction and Language Understanding in Large Language Models?

Shaina Raza, Oluwanifemi Bamgbose+4 more|arXiv (Cornell University)|Apr 1, 2024

Topic Modeling4 citations

TL;DR

This paper introduces SR$_{\text{LLM}}$, a safety-focused large language model fine-tuned using a novel, expert-annotated safety risk taxonomy and instruction-based, parameter-efficient fine-tuning (PEFT). The model significantly reduces unsafe outputs across five benchmark and two proprietary datasets while maintaining strong language understanding, demonstrating that bias reduction and robust language generation can be effectively balanced.

ABSTRACT

Large Language Models (LLMs) have advanced various Natural Language Processing (NLP) tasks, such as text generation and translation, among others. However, these models often generate texts that can perpetuate biases. Existing approaches to mitigate these biases usually compromise knowledge retention. This study explores whether LLMs can produce safe, unbiased outputs without sacrificing knowledge or comprehension. We introduce the Safe and Responsible Large Language Model ( extbf{SR}$_{ ext{LLM}}$), which has been instruction fine-tuned atop of a safe fine-tuned auto-regressive decoder-only LLM to reduce biases in generated texts. We developed a specialized dataset with examples of unsafe and corresponding safe variations to train extbf{SR}$_{ ext{LLM}}$ to identify and correct biased text. Experiments on our specialized dataset and out-of-distribution test sets reveal that extbf{SR}$_{ ext{LLM}}$ effectively reduces biases while preserving knowledge integrity. This performance surpasses that of traditional fine-tuning of smaller language models and base LLMs that merely reply on prompting techniques. Our findings demonstrate that instruction fine-tuning on custom datasets tailored for tasks such as debiasing is a highly effective strategy for minimizing bias in LLM while preserving their inherent knowledge and capabilities. The code and dataset are accessible at \href{https://github.com/shainarazavi/Safe-Responsible-LLM}{SR-LLM}

Motivation & Objective

• To address the growing risks of unsafe, biased, or toxic outputs in large language models (LLMs) due to misalignment with human values.
• To close the gap in existing LLM safety research by creating a specialized, expert-annotated dataset for safety-focused instruction tuning.
• To develop a parameter-efficient, instruction-tuned LLM that balances reduced bias with strong language understanding capabilities.
• To evaluate the model’s safety performance across diverse benchmarks and real-world proprietary datasets.
• To promote responsible AI advancement through open-sourcing data, code, and transparent evaluation protocols.

Proposed method

• Developed a comprehensive safety risk taxonomy to systematically categorize unsafe LLM outputs into distinct risk types, including toxicity, bias, negative sentiment, and harmful content.
• Constructed a new instruction-tuning dataset with expert-annotated pairs of unsafe prompts and their corresponding benign, safe variations to guide model behavior.
• Applied parameter-efficient fine-tuning (PEFT) techniques, such as LoRA, to adapt the base LLM with minimal parameter updates, ensuring efficiency and scalability.
• Integrated instruction tuning with both unsafe and safe example pairs to enable the model to learn to correct harmful outputs while preserving linguistic fluency.
• Employed a multi-stage evaluation pipeline using five public benchmarks and two proprietary datasets to measure safety improvements and language understanding.
• Used red-teaming and adversarial prompting to test robustness against jailbreak attempts and prompt injection attacks.

Experimental results

Research questions

• RQ1 Can a safety-focused instruction-tuned LLM significantly reduce the generation of biased, toxic, or harmful content without degrading language understanding?
• RQ2 How effective is a custom, expert-annotated dataset of unsafe-to-safe prompt pairs in improving LLM safety compared to standard fine-tuning approaches?
• RQ3 To what extent can parameter-efficient fine-tuning (PEFT) maintain model performance while enabling safe, scalable deployment of LLMs?
• RQ4 How does SR$_{\text{LLM}}$ perform under adversarial conditions such as prompt injection or jailbreak attacks?
• RQ5 Can a unified safety risk taxonomy guide the systematic identification and mitigation of diverse LLM safety issues?

Key findings

• SR$_{\text{LLM}}$ achieved a notable reduction in unsafe content generation across all five benchmark datasets and both proprietary datasets, demonstrating consistent safety improvements.
• The model showed significant improvement in generating safe, benign responses to harmful or biased prompts, particularly in categories like gender and racial bias, toxicity, and negative sentiment.
• Through PEFT, the model maintained strong language understanding capabilities, indicating that safety fine-tuning does not inherently compromise model performance.
• The model exhibited robustness against adversarial prompt injection and jailbreak attempts, suggesting enhanced resilience compared to baseline models.
• The expert-annotated dataset of unsafe-to-safe prompt pairs proved effective in guiding the model to learn safe response generation, validating the utility of curated safety data.
• The open-sourcing of the dataset and code enables reproducibility and community-driven progress in responsible AI development.