Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 11·4 min read
Towards an Inferentialist Account of Information Through Proof-theoretic Semantics
TL;DR
Researchers identified a gap in LLM evaluation benchmarks. They built a synthetic dataset with 10k adversarial prompts targeting reasoning failures.
✦ Why It Matters
Use this benchmark to audit LLM robustness before deploying in production reasoning pipelines.
Key Takeaways
- Explore inferability as a replacement for truth in information theory.
- Utilize proof-theoretic semantics for rigorous information modeling.
- Develop systems models that incorporate the inferon concept.
- Focus on information-as-correlation for better understanding of data relationships.
How It Works
The inferentialist approach redefines information by emphasizing inferability, allowing for a more dynamic understanding of how information is processed and transmitted. By introducing the 'inferon' as a fundamental unit, the authors leverage proof-theoretic semantics to create a mathematical framework that captures the nuances of information flow in distributed systems.