Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 15·5 min read
A Benchmark and Framework for Evaluating Next Action Predictions in Spreadsheets
TL;DR
Spreadsheets lack predictive features that could enhance user efficiency. A benchmark was created to evaluate next action predictions based on user action sequences, addressing challenges like limited edit histories and complex action types.
✦ Why It Matters
Engineers can leverage this benchmark to develop more effective predictive features for spreadsheet applications.
Key Takeaways
- Utilize the benchmark for developing predictive features in spreadsheets.
- Explore the impact of user profiles on prediction accuracy.
- Implement real-time feedback mechanisms for user actions.
- Analyze the efficiency of different prediction models.
Full Summary
Predictive code completion is common in programming but largely absent in spreadsheet applications, which are widely used. To fill this gap, a benchmark was developed to assess systems that predict future user actions in spreadsheets based on observed sequences.
The authors curated 52 sequences totaling 12,000 actions from public spreadsheet data, overcoming the challenge of limited edit histories. They introduced an online evaluation method where predictions are made after each user action, allowing for immediate feedback and adjustment.
Various baseline predictors, including zero-shot large language models (LLMs) and fine-tuned models, were tested. The findings highlighted key aspects such as the efficiency of predictions, the impact of user profiles, and the occurrence of false positives.
This research provides a structured approach for improving predictive features in spreadsheet applications.