arxiv_ai 96% Match Research Paper AI safety researchers,ML interpretability researchers,LLM developers,AI ethicists 2 weeks ago

Towards Understanding Safety Alignment: A Mechanistic Perspective from Safety Neurons

ai-safety › interpretability

📄 Abstract

Abstract: Large language models (LLMs) excel in various capabilities but pose safety risks such as generating harmful content and misinformation, even after safety alignment. In this paper, we explore the inner mechanisms of safety alignment through the lens of mechanistic interpretability, focusing on identifying and analyzing safety neurons within LLMs that are responsible for safety behaviors. We propose inference-time activation contrasting to locate these neurons and dynamic activation patching to evaluate their causal effects on model safety. Experiments on multiple prevalent LLMs demonstrate that we can consistently identify about $5\%$ safety neurons, and by only patching their activations we can restore over $90\%$ of the safety performance across various red-teaming benchmarks without influencing general ability. The finding of safety neurons also helps explain the ''alignment tax'' phenomenon by revealing that the key neurons for model safety and helpfulness significantly overlap, yet they require different activation patterns for the same neurons. Furthermore, we demonstrate an application of our findings in safeguarding LLMs by detecting unsafe outputs before generation. The source code is available at https://github.com/THU-KEG/SafetyNeuron.

Authors (6)

Jianhui Chen

Xiaozhi Wang

Zijun Yao

Yushi Bai

Lei Hou

Juanzi Li

Submitted

June 20, 2024

arXiv Category

cs.CL

arXiv PDF

Key Contributions

This paper provides a mechanistic perspective on safety alignment in LLMs by identifying and analyzing 'safety neurons' responsible for safe behaviors. Using activation patching, it demonstrates that these neurons causally influence safety performance, restoring over 90% of safety across benchmarks without significantly impacting general abilities, and helps explain the 'alignment tax'.

Business Value

Enhances trust and reliability in LLMs by providing methods to understand and verify their safety mechanisms, crucial for widespread adoption in sensitive applications.

Paper Metadata

Innovation Type

Methodological

Deployment Feasibility

The methods are analytical tools for understanding existing models, not direct deployment components. Requires expertise in interpretability techniques.

Limitations Addressed

Addresses the lack of understanding of *how* safety alignment works in LLMs, the 'black box' nature of safety mechanisms, and the trade-offs (alignment tax) associated with safety training.

Performance Gains

Restored over 90% of safety performance across various red-teaming benchmarks by patching safety neuron activations.

Technical Tags

safety alignmentmechanistic interpretabilitysafety neuronsLLM safetyactivation patchingred-teamingalignment taxcausal analysisneural network interpretability

Research Topics

AI SafetyMachine Learning InterpretabilityLarge Language ModelsModel RobustnessAI Ethics

Methods & Architectures

Mechanistic InterpretabilityInference-time activation contrastingDynamic activation patchingCausal effect evaluation Large Language Models (LLMs)

Applications & Tasks

AI Safety Responsible AI Natural Language Processing Understanding safety mechanisms in LLMsIdentifying causal factors for safety behaviorsQuantifying the impact of safety alignmentExplaining the 'alignment tax' Locating safety neuronsEvaluating causal effects of safety neuronsRestoring safety performanceAnalyzing alignment tax

Datasets & Benchmarks

Benchmarks

red-teaming benchmarks

Safety performanceGeneral ability performance

Related Fields

Explainable AI (XAI)AI EthicsMachine Learning SecurityNeuroscience

Keywords

safety alignmentmechanistic interpretabilitysafety neuronsLLM safetyactivation patchingred-teamingalignment taxcausal inferenceneural networksAI safetyinterpretability

Academic Context

#AI Safety#Machine Learning Interpretability#Large Language Models#Model Robustness#AI Ethics

Commercial Potential

Potential Products

AI safety auditing toolsModel interpretability platforms

Target Industries

TechnologyAI Development any industry deploying LLMs

Use Case Examples

Auditing LLMs for safety vulnerabilitiesDeveloping methods to improve LLM safety without sacrificing performanceUnderstanding why LLMs sometimes generate harmful content

Competitive Edge

Offers a deeper, mechanistic understanding of safety alignment compared to purely empirical or behavioral approaches.

Resource Requirements

Compute Needs

Requires significant computational resources for running LLMs and performing activation analysis.

Data Requirements

Access to pre-trained LLMs, red-teaming datasets.

Deployment Constraints

Interpretability methods can be computationally intensive and require specialized knowledge.

Scalability

Scalability of interpretability methods can be a challenge for extremely large models.

Production Readiness

Maturity Level

Research Prototype

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