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The Future of AI and PowerPoint: How Large Language Models Could Boost Presentation Building

How integration of LLMs like Copilot could completely transform PowerPoint, saving users time while unlocking more creativity.

PowerPoint remains an indispensable tool for creating compelling presentations in business, education, and other domains.

However, building professional slides from scratch can be an incredibly tedious and time-consuming process.

This typically involves:

Step 1 — Manually laying out titles, text, and basic shapes slide-by-slide

Step 2 — Inserting visuals like charts, diagrams, and images

Step 3 — Styling and formatting each element for visual cohesion

Step 4 — Ensuring a logical flow and clear messaging tailored to the audience

This entire process can take hours of repetitive manual effort even for experienced presenters.

Fortunately, an emerging technology called large language models (LLMs) could soon automate and streamline much of this workflow. Recently, Microsoft unveiled its powerful Copilot LLM into Office apps like PowerPoint to assist with content generation.

In this article, we’ll explore how integration of LLMs like Copilot could completely transform PowerPoint, potentially saving users tremendous time while unlocking more creativity. We use Guo et al. 2023 Study.

The Method to assess LLMs capabilities:

The PPTX-Match Evaluation System is designed to assess whether an LLM successfully completes a PowerPoint task based on the user’s instructions. The key steps are:

  1. The LLM processes the current instruction, PPT file content, and API reference file.
  2. It then generates an API sequence to execute the instructed task.
  3. These APIs are executed, modifying the PPT file to create a prediction file (Pred File).
  4. The evaluation system compares the prediction file to the intended label output file. This involves:
  • Position Relation Checker: Validates spatial relationships between objects using coordinate calculations and boolean checks. For example, it may verify Object A is above Object B based on their Y-coordinates.
  • Attribute Content Checker: Iterates through attributes of shapes in the prediction and label files. It extracts the content using extract_content() and compares them using string_match() to identify mismatches.
  1. If no mismatches are found in attribute content or spatial relationships, the task is considered successfully completed.

The LLM must correctly understand the instructions, PPT state, and necessary APIs to generate a prediction file that matches the label output under comprehensive content and positional checking. This end-to-end evaluation workflow assesses real task completion ability beyond just API sequence generation.

The study:

  1. Created a dataset of multi-turn dialogues with instructions for creating/editing PPT files.
  2. Developed an automated system to judge if LLMs successfully completed each instruction by comparing the output file to the correct version.
  3. Tested LLMs like GPT-3 on one-off tasks and multi-turn dialogues to assess strengths and limitations.

Key Results

The study yielded several key findings:

  • LLMs could complete simple one-off instructions fairly well like adding text.
  • Tracking long conversations with multiple tasks proved difficult, causing cascading errors.
  • LLMs struggled with spatial reasoning like positioning objects based on descriptions.
  • Multi-modal instructions involving images or charts posed challenges.

These limitations highlight areas for improvement through further training.

The Promise: How LLMs Could Transform PowerPoint Creation

Despite current limitations, LLMs have immense potential for:

  • Automating repetitive slide generation and design tweaks
  • Producing initial slide drafts from outlines and raw content
  • Suggesting creative visuals, layouts, and phrasing tailored to the audience
  • Streamlining team collaboration via conversational slide review and editing
  • Dynamically generating presentations based on speaker and context

With sufficient progress, LLMs could become indispensable AI assistants for assembling polished slides. Expert design knowledge codified into models could amplify every user’s abilities.

Next Steps for Developing PowerPoint-Proficient LLMs

Realizing this future requires focused research including:

  • Training models on presentation building logic and best practices
  • Improving spatial reasoning and multimodal intelligence
  • Ensuring models avoid egregious errors
  • Developing interfaces for conversational slide editing


This study introducing the PowerPoint Task Completion (PPTC) benchmark yields valuable insights into the current capabilities and limitations of large language models (LLMs) for interactive presentation building.

The proposed evaluation methodology provides a robust assessment of end-to-end task completion spanning instruction comprehension, API selection, PPT file manipulation, and final output validation. Testing on models like GPT-3 reveals promising aptitude for simple standalone commands, but difficulty tracking context throughout complex multi-turn dialogues.

Several clear challenges emerge for LLMs including spatial reasoning for positioning shapes, multimodal content manipulation, avoiding error propagation across long conversations, and processing lengthy slide templates. However, the research also highlights the immense potential of LLMs to automate, enhance, and creatively augment many repetitive aspects of presentation building if these weaknesses can be addressed.

Realizing this future will require focused efforts to train models on presentation design logic, technical constraints, layout best practices, and aesthetic principles. Advances in spatial perception, reasoning across modalities, and conversational memory are critical. Close integration with PowerPoint’s native capabilities will enable seamless collaboration between human creators and AI assistants.

With sufficient progress, LLM-empowered systems could significantly amplify author productivity and lower the expertise bar for developing polished, high-impact slides. But thoughtfully managing risks around bias, safety, and misinformation remains paramount as this technology matures. Done responsibly, integrating intelligence into PowerPoint could profoundly transform business communication, education, and beyond.

This study provides an important foundation and springboard for realizing this vision. The PPTC benchmark introduced here will allow ongoing measurement of progress as LLMs continue evolving. If core weaknesses are overcome, an exciting future likely awaits where AI and PowerPoint unite to augment human creativity and productivity.

Thank you for reading!

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