LLM Resilience Evaluation Framework: Testing the Response Stability of Large Language Models

llm-resilience-eval is an open-source framework focused on evaluating the response stability of large language models (LLMs) under semantics-preserving perturbations, supporting test scenarios such as paraphrasing, flattery, distractor, and confirmation challenge. This framework aims to address the problem of inconsistent responses caused by minor input changes in real-world LLM applications, enhancing model reliability and AI safety.

Large language models face the challenge of unstable responses to minor input changes in real-world applications—for example, different user expressions, redundant information, or biased wording may lead to drastic output changes. This lack of resilience can cause serious consequences in scenarios like medical diagnosis, legal consultation, and educational tutoring, so evaluating LLM response resilience has become an important topic in AI safety and reliability research.

1. Paraphrasing Perturbation

Maintain semantic consistency through synonym replacement, sentence structure adjustment, etc., to test whether the model relies on specific vocabulary rather than understanding the essence (e.g., "Optimize Python code performance" is paraphrased as "Improve Python program running efficiency").

2. Flattery Perturbation

Implant user-biased opinions to test whether the model caters to users and deviates from facts, evaluating objectivity and safety.

3. Distractor Perturbation

Add irrelevant information to test the model's ability to filter noise and focus on core issues.

4. Confirmation Challenge

Require verification of the truthfulness of statements to test fact-checking ability and awareness of knowledge boundaries.

1. Baseline Dataset Construction: Use preset or custom standard question sets with clear answers.
2. Perturbation Generation: Automatically generate multiple semantically consistent perturbation variants and verify them.
3. Response Collection: Submit original and perturbed questions in batches and collect model responses.
4. Consistency Measurement: Measure response consistency through semantic similarity, answer equivalence, manual evaluation, etc.
5. Resilience Scoring: Generate an overall score and detailed report based on comprehensive performance.

• Model Selection Reference: Help enterprises select more reliable LLMs.
• Model Improvement Guidance: Identify weak points and optimize training data or fine-tuning strategies in a targeted manner.
• Security Audit Tool: Reliability testing before deployment in sensitive scenarios.
• Academic Research: Provide standardized test benchmarks to facilitate result comparison and reproduction.

Technical Implementation Features

• Modular design: perturbation types are independent and extensible;
• Configurability: supports adjustment of perturbation parameters;
• Multi-model compatibility: adapts to OpenAI API, local models, etc.;
• Reproducibility: fixed random seeds;
• Automatic reporting: generates visual analysis reports.

Relationship with Related Work

Different from HELM (comprehensive evaluation), BIG-bench (large-scale benchmark), and TruthfulQA (truthfulness testing), this framework focuses on response stability under semantic perturbations and serves as a supplement to comprehensive evaluations.

Usage Suggestions

1. Start with standard test sets to familiarize yourself with the framework;
2. Design targeted perturbations combined with business scenarios;
3. Incorporate into post-deployment continuous monitoring systems;
4. Regularly compare the resilience performance of different models.

Future Outlook

As LLMs expand their applications in key fields, resilience evaluation will become an essential part of model quality standards, promoting the industry's focus on reliability and ultimately benefiting end users.