Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·May 26·5 min read
TS-Skill: A Benchmark for Evaluating Analytical Skills in Time-Series Question Answering
TL;DR
Existing benchmarks for time-series question answering (TSQA) do not effectively evaluate the specific analytical skills needed for temporal reasoning. TS-Skill is a new benchmark that assesses three key skills: temporal scale selection, temporal localization, and cross-interval integration.
✦ Why It Matters
Engineers can use TS-Skill to better assess and improve the temporal reasoning capabilities of their models.
Key Takeaways
- Utilize TS-Skill to evaluate model performance on specific analytical skills.
- Focus on improving temporal localization and cross-interval integration in models.
- Incorporate human validation in question-answering frameworks for quality assurance.
- Explore the SKEvol framework for generating time-series questions and answers.
Full Summary
Time-series question answering (TSQA) involves answering questions based on data that varies over time, which requires models to understand complex temporal patterns. Current benchmarks often categorize tasks broadly, making it hard to pinpoint specific analytical skills that models lack.
To address this, TS-Skill was created to evaluate three distinct skills: temporal scale selection (SK1), which involves choosing the right time frame; temporal localization (SK2), which focuses on identifying specific time points; and cross-interval integration (SK3), which requires synthesizing information from different time periods. The benchmark was constructed using a framework called SKEvol, which generates questions and answers while ensuring quality through human validation.
Testing on ten advanced language models showed that while models performed variably across the skills, SK3 consistently posed challenges, especially for non-agent models. These findings suggest that a skill-level evaluation can reveal weaknesses in temporal reasoning that are not apparent from overall performance scores.