Intent Preservation Benchmark: Evaluating Large Language Models' Ability to Preserve Human Intent in High-Risk Scenarios

Core Information

• Project Name: Intent Preservation Benchmark
• Positioning: Open-source benchmark and evaluation framework for measuring large language models (LLM) ability to preserve human intent in high-risk scenarios like healthcare, finance, and government
• Purpose: Help researchers identify the risk of intent drift in AI systems before deployment
• Source: GitHub (Author/Maintainer: khiannadeseide, Release Date: 2026-07-12, Original Link: https://github.com/khiannadeseide/Intent-Preservation-Benchmark.krd)
• Keywords: AI safety, intent preservation, large language models, benchmarking, healthcare AI, high-risk environments, evaluation framework, AI alignment, clinical AI

This benchmark focuses on AI intent alignment issues in high-risk scenarios and is an important tool to ensure the safety and reliability of AI systems.

As LLMs are increasingly integrated into high-risk fields like healthcare, finance, and government, a key question emerges: Do AI systems truly understand and preserve the original intent of human users?

In practical applications, intent drift can lead to serious consequences:

• Healthcare scenario: A doctor asks about the risks of a certain treatment plan, but the AI provides an irrelevant alternative
• Finance scenario: A user asks about conservative investment strategies, but the AI recommends high-risk products These seemingly minor deviations can have catastrophic consequences in high-risk environments.

Intent Preservation Benchmark is an open-source evaluation framework designed to help researchers determine whether AI systems faithfully preserve human intent before deployment to high-risk environments. The project provides standardized testing methods and evaluation metrics, enabling developers to quantitatively measure models' intent preservation ability.

Core Objectives

• Intent Preservation Measurement: Quantitatively evaluate LLMs' ability to preserve original intent in complex interactions
• High-Risk Scenario Coverage: Focus on practical use cases in key fields like healthcare, government, and finance
• Reproducible Evaluation: Provide standardized benchmarking processes to ensure comparable results
• Clinical AI Safety: Include specialized clinical AI safety evaluation standards

Intent vs. Instruction

Traditional AI evaluation often focuses on whether models correctly execute instructions (Instruction Following), but intent preservation is a deeper requirement—models must not only execute instructions but also understand the true purpose and context behind them.

Examples:

• Instruction level: "List drugs for treating hypertension"
• Intent level: The patient may want to know options with the least side effects, or the most economical plan If the model only focuses on the literal instruction, it will ignore the user's real needs.

Specificity of High-Risk Environments

In high-risk fields like healthcare, finance, and law, the cost of intent drift is extremely high:

1. Healthcare scenario: Misunderstanding a doctor's intent may lead to incorrect diagnostic recommendations
2. Finance scenario: Misinterpreting a user's risk preference may lead to inappropriate investment advice
3. Government services: Misunderstanding citizens' needs may affect the quality of public services

Multi-Dimensional Evaluation System

Intent Preservation Benchmark uses a multi-dimensional evaluation method to test models' intent understanding ability from the following perspectives:

1. Semantic Consistency: Whether the model's output is semantically consistent with the user's intent
2. Context Understanding: Whether the model correctly understands the conversation history and background information
3. Goal Alignment: Whether the model advances toward the user's expected goal
4. Safety Boundaries: Whether the model adheres to safety constraints while preserving intent

Clinical AI Safety Evaluation Standards

The project specifically includes the Clinical AI Safety Evaluation Rubric, addressing the uniqueness of healthcare scenarios:

• Patient privacy protection
• Accuracy of medical evidence
• Appropriateness of treatment recommendations
• Emergency handling

Benchmark Structure

The project codebase includes the following core components:

• benchmark/: Benchmark dataset and evaluation scripts
• docs/: Documentation and usage guides
• Clinical AI Safety Evaluation Rubric: Document for clinical safety evaluation standards

Usage Process

1. Scenario Definition: Define specific usage scenarios and intent types
2. Test Case Construction: Create test cases covering various intent drift risks
3. Model Evaluation: Run the benchmark and collect model responses
4. Result Analysis: Use the evaluation framework to analyze intent preservation ability
5. Iterative Improvement: Optimize the model or prompt strategy based on evaluation results

For AI Developers

• Identify potential intent drift risks before deployment
• Compare the intent preservation performance of different models
• Verify the effectiveness of fine-tuning or prompt engineering

For Enterprise Users

• Evaluate the intent preservation ability of vendor models
• Establish quality thresholds for internal AI systems
• Meet compliance requirements (especially in regulated industries)

For Researchers

• Promote academic research on intent preservation technology
• Establish industry standards and best practices
• Facilitate fair comparisons across models and methods

Current Limitations

• Evaluation scenarios are mainly focused on English environments
• There is room to expand coverage of high-risk fields
• Quantitative indicators for intent preservation are still being refined

Future Directions

1. Multilingual Support: Expand to more languages like Chinese and Spanish
2. Industry Expansion: Cover more high-risk fields like law and education
3. Real-Time Evaluation: Develop runtime intent preservation monitoring tools
4. User Research: Optimize evaluation standards by incorporating real user feedback