ProbingReflection

Source code for the paper "From Emergence to Control: Probing and Modulating Self-Reflection in Language Models"

Overview

This project investigates self-reflection in Large Language Models (LLMs) through probing and steering techniques. We explore how reflection behaviors emerge and how they can be controlled through vector-based interventions.

Key Concepts

• Probing Vectors: Techniques to detect and measure self-reflection patterns in model activations
• Model Insertion: Methods for injecting steering vectors to modulate reflection behavior
• Reflection Analysis: Frameworks for evaluating and understanding model self-reflection

Installation

uv sync

Development

# Lint check
uv run ruff check src/ tests/

# Format
uv run ruff format src/ tests/

# Type check
uv run mypy src/

# Run tests
uv run pytest

Project Structure

.
├── src/probing_reflection/    # Source code
│   ├── __init__.py
│   └── py.typed
├── tests/                     # Test files
├── docs/                      # Documentation
│   └── design-docs/           # Design documents
├── AGENTS.md                  # AI agent instructions
├── ARCHITECTURE.md            # System architecture
└── pyproject.toml             # Project configuration

Research Workflow

This project follows an AI-assisted research workflow. See AGENTS.md for detailed instructions on how AI agents should work in this repository.

Citation

If you use this code, please cite:

@article{probing_reflection_2024,
  title={From Emergence to Control: Probing and Modulating Self-Reflection in Language Models},
  author={[Authors]},
  year={2024}
}

License

[Add your license here]