Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 12·5 min read
Representing Time Series as Structured Programs for LLM Reasoning
TL;DR
Existing methods for time series analysis often require large language models (LLMs) to extract temporal structures from raw data, leading to inefficiencies. The Time-Series-to-Structured-Program (T2SP) representation decomposes time series into trends and events, allowing LLMs to reason about them more effectively.
✦ Why It Matters
Engineers can utilize T2SP to improve LLM performance in time-series analysis without extensive retraining.
Key Takeaways
- Adopt T2SP for better time series representation in LLMs.
- Reduce computational overhead by using structured programs.
- Leverage existing LLM capabilities for time-series reasoning.
- Evaluate performance improvements in specific reasoning tasks.
How It Works
T2SP transforms time series data into a structured format by identifying and organizing trends, periodic patterns, and significant events. This structured representation allows LLMs to process and reason about time series more effectively, as it aligns with their training on textual and code-like data.