Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
Implicit Causal Graph Construction in Text via Chain Discovery
TL;DR
Causal graphs in text often rely on observable events, leaving a gap in understanding implicit relationships. This study introduces a method for constructing implicit causal graphs using large language models (LLMs) to infer hidden causal events from cause-effect pairs.
✦ Why It Matters
Engineers can leverage this method to uncover implicit causal relationships in text data, enhancing their models' interpretability.
Key Takeaways
- Utilize LLMs to infer intermediate causal events from text.
- Explore both end-to-end graph construction and causal chain discovery methods.
- Leverage multiple LLMs for improved causal inference accuracy.
- Evaluate inferred causal relations against a curated database for reliability.
How It Works
The study treats cause-effect pairs as endpoints of a latent causal graph, using LLMs to infer the intermediate events that connect them. This allows for the construction of causal graphs that reflect implicit relationships in the text, rather than relying solely on observable events.