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📜 Talk to Your Slides:

Language-Driven Agents for Efficient Slide Editing

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📄 Research Paper (arXiv preprint)

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Note: TSBench-Hard version out! 📎 Download TSBench-Hard on Google Drive

Note: Batch slide inferece examples available.📎 Download Examples on Google Drive

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📖 Overview

Editing presentation slides remains one of the most common and time-consuming tasks faced by millions of users daily, despite significant advances in automated slide generation.

While GUI-based agents have demonstrated visual control capabilities, they often suffer from high computational cost and latency. To address this, we propose Talk-to-Your-Slides, an LLM-powered agent that edits slides in active PowerPoint sessions by leveraging structured object-level information—bypassing the need for visual pixel interaction.

Our system introduces a hierarchical editing design, separating high-level semantic planning from low-level object manipulation. This allows:

• 🚀 34.02% faster execution
• 🎯 34.76% better instruction adherence
• 💸 87.42% cheaper operations

To evaluate slide editing performance, we present TSBench, a human-annotated benchmark with 379 diverse instructions spanning four major categories.

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📚 TSBench Benchmark Dataset

TSBench (Original)

📎 Download TSBench on Google Drive

Our human-annotated benchmark with 379 diverse instructions spanning four major categories for evaluating slide editing performance.

TSBench-Hard

📎 Download TSBench-Hard on Google Drive

TSBench-Hard is an advanced evaluation subset designed to rigorously assess model robustness on complex real-world scenarios. This dataset contains 300 challenging instances across four key difficulty dimensions:

• Visual-Dependent Tasks: Instructions requiring spatial reasoning (e.g., "Align the text box to the left edge of the image")
• Ambiguous Instructions: High-level commands requiring inference (e.g., "Make the title slide look more professional")
• Complex Multi-step Logic: Tasks involving conditional formatting across multiple slides (e.g., "Apply bold formatting to all titles on slides that contain a table and if you think that is important, color into red")
• Impossible Tasks: Technically unfeasible requests (e.g., "Change the video content inside the embedded player") to evaluate the agent's ability to correctly identify and refuse invalid actions

Dataset Structure

Each instance in TSBench-Hard follows the structure:

{
  "instruction": "User command for slide editing task",
  "ideal_description": "Description of the ideal presentation after completing the task"
}

• instruction: Generated using GPT-4.1, then filtered by human evaluators to ensure quality and challenge level
• ideal_description: Describes the expected state of the presentation after successfully executing the instruction, generated by Gemini 2.5 Flash. This serves as the ground truth for evaluation
• The ideal_description can be used as the evaluation ground truth to assess whether an agent's output matches the expected ideal presentation state

🎬 Demo Videos

CamelCase
Prompt: “Please update all English on ppt slides number 7 to camelCase formatting.”

Only English → Blue
Prompt: “Please change only English into blue color in slide number 3.”

Typo Checking & Correction
Prompt: “Please check ppt slides number 4 for any typos or errors, correct them.”

Translate to English
Prompt: “Please translate ppt slides number 5 into English.”

Slide Notes Script
Prompt: “Please create a full script for ppt slides number 3 and add the script to the slide notes.”

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🛠️ Installation Guide

🖥️ Recommended: Python on Windows

⚠️ To allow Python to control PowerPoint via COM interface, you must enable VBA access:

1. Open PowerPoint
2. Go to File > Options > Trust Center > Trust Center Settings
3. In Macro Settings, check:
   • ✅ "Trust access to the VBA project object model"

📦 Setup Instructions

Step 1: Install Dependencies

pip install -r requirements.txt

Note: If you encounter issues with package installation, install these core packages:

pip install openai==1.74.0 google-generativeai anthropic python-pptx Flask python-dotenv pyyaml

Step 2: Configure API Keys

Option A: Using credentials.yml (Recommended)

Copy the example credentials file:

cp credentials.yml.example credentials.yml

Edit credentials.yml with your API keys:

gpt-4.1-mini:
  api_key:  "YOUR_OPENAI_API_KEY"
  base_url: "https://api.openai.com/v1"

gpt-4.1:
  api_key:  "YOUR_OPENAI_API_KEY"
  base_url: "https://api.openai.com/v1"

gemini-1.5-flash:
  api_key: "YOUR_GEMINI_API_KEY"

claude-3.7-sonnet:
  api_key: "YOUR_ANTHROPIC_API_KEY"

Option B: Using .env file

Create a .env file in the pptagent/ directory:

cd pptagent
cat > .env << EOF
OPENAI_API_KEY=your_openai_key_here
ANTHROPIC_API_KEY=your_anthropic_key_here
GEMINI_API_KEY=your_gemini_key_here
EOF

Step 3: Run the System

Web UI (Flask) - Recommended for interactive use:

python pptagent/main_flask.py

Then open your browser to http://localhost:8080

CLI Mode - For batch processing:

cd pptagent
python main_cli.py

Quick Start (shows usage):

python pptagent/main.py

🔧 Project Structure

Talk-to-Your-Slides/
├── pptagent/
│   ├── main.py              # Entry point (shows usage)
│   ├── main_flask.py        # Web UI server (Flask)
│   ├── main_cli.py          # CLI interface
│   ├── classes.py           # Core PPT agent classes
│   ├── test_Applier.py      # Applier implementations
│   ├── llm_api.py           # LLM API wrappers
│   ├── gemini_api.py        # Gemini-specific API
│   ├── utils.py             # Utility functions
│   ├── prompt.py            # System prompts
│   └── templates/           # Flask HTML templates
├── credentials.yml.example  # Example API credentials
├── requirements.txt         # Python dependencies
└── README.md               # This file

🎯 Supported Models

• OpenAI: GPT-4.1, GPT-4.1-mini, GPT-4.1-nano
• Google: Gemini 1.5 Flash, Gemini 2.5 Flash
• Anthropic: Claude 3.7 Sonnet

💡 Usage Examples

Example 1: Translate slide content

"Translate all text content on slide 1 into Korean."

Example 2: Fix typos

"Check slide 4 for any typos or errors and correct them."

Example 3: Change formatting

"Change all English text to blue color on slide 3."

See demo videos below for more examples!

🐛 Troubleshooting

Issue: ModuleNotFoundError for openai or google.generativeai

# Solution: Install missing packages
pip install openai==1.74.0 google-generativeai

Issue: FileNotFoundError for credentials.yml

# Solution: Create credentials file from example
cp credentials.yml.example credentials.yml
# Then edit credentials.yml with your API keys

Issue: COM error on Windows

• Make sure PowerPoint is installed
• Enable VBA access (see installation guide above)
• Run Python as Administrator if needed

Issue: Flask server not starting

# Check if port 8080 is available
# Try a different port by editing main_flask.py line 341:
# app.run(debug=True, port=8081)  # Change to different port

🏗️ Code Architecture

The system follows a hierarchical pipeline:

1. Planner: Analyzes user request and creates high-level plan
2. Parser: Parses the plan into structured tasks
3. Processor: Processes each task with contextual information
4. Applier: Applies changes to PowerPoint slides via COM/python-pptx
5. Reporter: Generates summary of changes made

Each component is modular and can be extended independently.

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