Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions (2309.07875v3)

Abstract: Training LLMs to follow instructions makes them perform better on a wide range of tasks and generally become more helpful. However, a perfectly helpful model will follow even the most malicious instructions and readily generate harmful content. In this paper, we raise concerns over the safety of models that only emphasize helpfulness, not harmlessness, in their instruction-tuning. We show that several popular instruction-tuned models are highly unsafe. Moreover, we show that adding just 3% safety examples (a few hundred demonstrations) when fine-tuning a model like LLaMA can substantially improve its safety. Our safety-tuning does not make models significantly less capable or helpful as measured by standard benchmarks. However, we do find exaggerated safety behaviours, where too much safety-tuning makes models refuse perfectly safe prompts if they superficially resemble unsafe ones. As a whole, our results illustrate trade-offs in training LLMs to be helpful and training them to be safe.

Safety-Tuned LLaMAs: Enhancing Safety in Instruction-Tuned LLMs

The paper "Safety-Tuned LLaMAs: Lessons From Improving the Safety of LLMs that Follow Instructions" explores the critical issue of safety in instruction-tuned LLMs. While these models, such as LLaMA and Falcon, have demonstrated remarkable capabilities in natural language understanding and generation, their potential for unsafe or harmful outputs when following malicious instructions presents significant risks. The authors explore the implications of prioritizing model helpfulness over safety and propose a method to improve the latter without compromising the former.

Key Findings

The paper outlines several important findings concerning safety-enhanced LLMs:

1. Trade-offs Between Helpfulness and Safety: The authors identify an inherent trade-off between making LLMs helpful and ensuring that they are safe. Models trained merely on general-purpose instruction-following datasets sometimes generate unsafe responses to harmful queries, such as instructions for illegal activities or containing harmful stereotypes.
2. Impact of Safety Examples on Model Behavior: By incorporating a minimal amount (approximately 3%) of safety-specific examples into the finetuning datasets, the authors substantially improve the safety of LLaMA models. This enhancement does not noticeably degrade the models' performance on standard benchmarks, confirming the feasibility of this approach.
3. Exaggerated Safety Responses: A noteworthy observation is that excessive safety-tuning can lead to exaggerated safety behaviors, where models decline safe queries that resemble unsafe ones. This phenomenon highlights the complexity of balancing helpfulness and safety in training paradigms.
4. Evaluation Framework: The paper presents newly developed datasets and evaluation methodologies to test model responses to unsafe prompts, controversial topics, and physical safety concerns. These datasets aim to rigorously quantify safety-related trade-offs in LLM outputs.

Methodological Approach

The authors utilize LLaMA and Falcon models, augmented with differing quantities of safety-focused data from a curated set, to assess the impact of safety training. They subsequently evaluate these models using both automated tools, such as a harmfulness reward model, and manual annotations. By leveraging OpenAI's content moderation API, they provide additional insights into the safety of model outputs.

Implications and Future Directions

The research findings carry significant implications for developing robust and safe AI systems:

• Practical Deployment: Incorporating safety training in LLM deployment appears viable without diminishing general task performance. This insight is vital for practitioners aiming to safely deploy these models in sensitive areas such as healthcare and customer support.
• Training Curriculum Design: A calculated inclusion of safety-oriented examples can feasibly mitigate unsafe behaviors, but requires careful calibration. Excessive safety data can impair the model's utility through exaggerated refusals, complicating the training process.
• Dataset and Evaluation Standardization: The authors underscore the necessity for standardized datasets and evaluation frameworks to consistently assess model safety across different applications and domains.

In the future, exploring novel fines strategies that decouple helpfulness and safety in LLMs, possibly through advanced reinforcement learning techniques, could further ameliorate this trade-off. Continued development in this area will ensure that LLMs remain valuable instruments that enhance productivity without compromising ethical standards or security.