Correcting Prompt Dependence in LLM Benchmarks: A Bayesian Hierarchical Model with Embedding-Space Clustering

2024-05-19 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

This paper introduces a principled, end-to-end framework for evaluating large language model (LLM) vulnerabilities to prompt injection attacks, addressing issues like non-comparable models, heuristic inputs, and uncertainty. The framework proposes practical experimental design approaches for fair LLM comparisons, considering scenarios for training or deploying LLMs by grouping them based on training data or performance. It also presents a Bayesian hierarchical model with embedding-space clustering to improve uncertainty quantification, especially with non-deterministic LLM outputs, imperfect test prompts, and limited compute. A case study comparing Transformer and Mamba architectures revealed that while accounting for output variability often reduces certainty, some attacks showed increased vulnerabilities in both Transformer and Mamba variants, even among LLMs with similar training data or mathematical abilities. For instance, a Transformer-Mamba-2 distilled model exhibited altered adversarial properties.

Key takeaway

For AI Security Engineers evaluating new LLM architectures, you should adopt a principled evaluation framework that accounts for confounding variables and quantifies uncertainty. Implement a Bayesian hierarchical model with embedding-space clustering to mitigate prompt bias and gain reliable insights into architectural vulnerabilities, especially with limited data. Your choice between Transformer and Mamba variants for robustness will depend on specific deployment requirements and the types of attacks prioritized.

Key insights

Reliable LLM vulnerability evaluation requires a Bayesian hierarchical model with embedding-space clustering to quantify uncertainty and mitigate prompt bias.

Principles

• Control confounding variables for fair LLM comparisons.
• Quantify uncertainty in LLM evaluations, especially with limited data.
• Mitigate prompt bias via embedding-space clustering.

Method

The method involves defining LLM groups, repeating trials, and applying a Bayesian hierarchical model with embedding-space clustering to identify distinct prompt concepts and quantify uncertainty.

In practice

• Match LLMs by training data or task performance for fair comparisons.
• Use embedding-space clustering to reduce prompt bias.
• Apply Bayesian models for robust evaluation with limited data.

Topics

• LLM Security Evaluation
• Prompt Injection Attacks
• Bayesian Hierarchical Models
• Embedding-Space Clustering
• Transformer Architecture
• Mamba Architecture
• Uncertainty Quantification

Code references

• leondz/garak
• EleutherAI/lm-evaluation-harness

Best for: Research Scientist, AI Scientist, AI Security Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.