Text Generation
Transformers
English
mixtral
legal
conversational
Inference Endpoints

Redactable-LLM

The high-level overview for integrating multiple Open Source Large Language Models within the AutoGen Framework is as follows:

Development of Custom Agents

  • Agent Design: Tasks include NLP/NER/PII identification, interpreting natural language commands, executing document redaction, and final verification.
  • Customization: Custom agents trained on specific tasks related to each aspect of the redaction process.
  • Human Interaction: Implement features to facilitate seamless human-agent interaction, allowing users to input commands and queries naturally (Optional)

LLM & VLLM AutoGen Integration

  • Model Selection: Automatic, task-dependent agent selection.
  • Enhanced Inference: Enhanced LLM inference features for optimal performance, including tuning, caching, error handling, and templating.
  • Quality Control: Vision agents analyze redacted documents using Set-of-Mark (SoM) prompting. Rejected documents are reprocessed and reviewed.
  • AutoGen Agents

System Optimization

  • Workflow Automation: Automate the redaction workflow using a blend of LLMs, custom agents, and human inputs for efficient detection and redaction of sensitive information.
  • Performance Maximization: Optimize the system for both efficiency and accuracy, utilizing AutoGen's complex workflow management features.

User Interface Development

  • Interface Design: Develop a user-friendly interface that enables non-technical users to interact with the system via natural language prompts.
  • Feedback Integration: Implement a feedback loop to continuously refine the system's accuracy and user-friendliness based on user inputs.
  • User Knowledgebase: (Optional) User account, profile, and domain knowledge will be accessible by the Research agent, for personalized interaction and results.

Training, Testing and Validation

  • Model Training: Develop new datasets, focused on document understanding related to redaction.
  • Unit Testing: Conduct extensive unit tests to ensure individual system components function correctly.
  • System Testing: Perform comprehensive end-to-end testing to validate the entire redaction process, from user input to output.
  • User Trials: Facilitate user trials to gather feedback and make necessary system adjustments.

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Datasets used to train redactable-llm/redactable-dolphin-mixtral