Project: Klarity
What is the project about?
Klarity is a toolkit designed to inspect and debug the decision-making processes of AI models. It helps users understand how models "think" and identify potential issues before deployment.
What problem does it solve?
It addresses the "black box" nature of AI models, providing explainability and insights into their reasoning, uncertainty, and visual attention (for vision-language models). This helps in identifying and mitigating errors, biases, and unexpected behaviors. It helps improve the reliability and trustworthiness of AI systems.
What are the features of the project?
- Dual Entropy Analysis: Measures model confidence using both raw entropy and semantic similarity.
- Reasoning Analysis: Extracts and evaluates the step-by-step reasoning patterns in model outputs.
- Visual Attention Analysis: Visualizes and analyzes how vision-language models focus on different parts of an image.
- Semantic Clustering: Groups similar predictions to reveal decision pathways.
- Structured Insights: Provides detailed JSON output of uncertainty patterns and reasoning steps.
- AI-powered Report: Uses capable models to interpret generation patterns and provide human-readable insights.
- Custom Analysis Configuration: Allows users to adjust parameters like minimum token probability and semantic similarity thresholds.
What are the technologies used in the project?
- Model Frameworks: Hugging Face Transformers, vLLM, Together AI (with planned support for PyTorch).
- Analysis Models: Hugging Face Transformers, Together AI API (with planned support for PyTorch).
- Programming Language: Python
- Dependencies: transformers, PIL, torch
What are the benefits of the project?
- Improved AI Explainability: Understand why a model makes a particular decision.
- Error Mitigation: Identify and fix issues before they impact users.
- Enhanced Trustworthiness: Build more reliable and transparent AI systems.
- Debugging and Optimization: Gain insights to improve model performance and training.
- Multi-Modal Support: Works with both language and vision-language models.
- Easy Integration: Simple API for integration with existing workflows.
What are the use cases of the project?
- Debugging Generative AI: Identifying the root causes of unexpected or incorrect outputs.
- Improving Training Data: Finding areas where the model is uncertain and needs more training examples.
- Model Auditing: Assessing model behavior for bias, fairness, and safety.
- Model Selection/Routing: Determining which model is most confident for a given input.
- Vision-Language Model Analysis: Understanding how visual inputs influence model predictions.
- Reasoning-Based Applications: Analyzing and improving the logical flow of models in tasks requiring multi-step reasoning.
- Preventing Hallucinations: Identifying and mitigating instances where the model generates false or nonsensical information.
